2025年第11期共收录72篇

1. Canal System Water Resource Optimization Considering Water Allocation Equity

Accession number: 20254919628292

Title of translation: 考虑配水公平性的渠系水资源优化配置研究

Authors: Zhang, Chenglong (1, 2); Yuan, Yuan (1, 2); Dong, Jiao (1, 2); Guo, Shanshan (1, 2); Huo, Zailin (1, 2)

Author affiliation: (1) College of Water Resources and Civil Engineering, China Agricultural University, Beijing; 100083, China; (2) State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing; 100083, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 687-695

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Irrigation districts consist of multi-level water conveyance networks and spatially heterogeneous farmlands, where water usage among farmlands under different canal systems exhibits game-theoretic characteristics. The pressing challenge is to develop a canal water resources optimization model that integrates dynamic channel conveyance properties with multi-objective coordination mechanisms, enabling multidimensional trade-offs among water allocation equity, conveyance loss, and main canal diversion stability. Taking a typical two-level canal system (Yonglan Sub-main Canal and its branch canals) in the Hetao Irrigation District as the research object, using the net flow rate, start time, and end time of water distribution to branch canals as decision variables, a multi-objective optimization model for canal water allocation that incorporated diversion stability, seepage loss, and water distribution equity was developed. The model advanced beyond existing canal distribution models by eliminating the assumption that the main canal must be in a “hydraulically continuous state” at water distribution onset. It incorporated main canal water transit time constraints and employed the non-dominated sorting genetic algorithm Ⅱ (NSGA Ⅱ) to derive optimized water allocation schemes. Results demonstrated that the model’s diversion schedule completed branch canal water distribution within 23. 15 days per irrigation rotation—a 29. 55% reduction compared with that of static models ignoring water transit constraints (32. 86 days). This significantly shortened irrigation duration and markedly decreased transit seepage losses. Furthermore, the model achieved a water distribution equity objective value of merely 0. 002 27 ( ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Economic and social effects

Controlled terms: Conveying? - ?Decision making? - ?Game theory? - ?Hydraulic structures? - ?Irrigation? - ?Irrigation canals? - ?Multiobjective optimization? - ?Screening? - ?Seepage? - ?Sorting ? - ?Water management? - ?Water supply systems

Uncontrolled terms: Canal systems? - ?Canal water? - ?Equity? - ?Irrigation unit? - ?Multi-objectives optimization? - ?Non-dominated sorting genetic algorithm ⅱ? - ?Non-dominated sorting genetic algorithms? - ?Two-level canal system? - ?Water allocations? - ?Water distributions

Classification code: 444 Water Resources? - ?446 Waterworks? - ?446.1 Water Supply Systems? - ?483 Soil Mechanics and Foundations? - ?692.1 Conveyors? - ?802.3 Chemical Operations? - ?821.4 Agricultural Methods? - ?912.2 Management? - ?971 Social Sciences? - ?1106.2 Data Handling and Data Processing? - ?1201.4 Applied Mathematics? - ?1201.7 Optimization Techniques

Numerical data indexing: Age 2.3564E-01yr, Age 4.11E-02yr, Percentage 1.00E00%, Percentage 5.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.067

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

2. Wheat Ear Recognition Method Using YOLO v8 TRP Model

Accession number: 20254919628133

Title of translation: 基于 YOLO v8 TRP 模型的小麦麦穗识别方法

Authors: Yuan, Yingchun (1, 2); Geng, Jun (1); Xu, Nan (1, 2); He, Zhenxue (1, 3); Wang, Kejian (1, 3)

Author affiliation: (1) College of Information Science and Technology, Hebei Agricultural University, Baoding; 071001, China; (2) Hebei Engineering Research Center for Agricultural Remote Sensing Application, Baoding; 071001, China; (3) Hebei Key Laboratory of Agricultural Big Data, Baoding; 071001, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 499-508

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issue of insufficient target feature extraction and low recognition accuracy due to the low saliency of individual wheat ears in images, an improved YOLO v8 TRP wheat ear recognition method based on YOLO v8n was proposed. A triple attention fusion (TAF) module before the backbone network was designed, which combined multi-head self-attention (MHSA), pixel attention (PA), and channel attention (CA) to fully enhance the contrast between the wheat ear target and the complex background without destroying the detailed information of the wheat ear. Additionally, the receptive field attention convolution (RFAConv) and CBAM attention mechanism were introduced to improve the convolution module in the backbone network, enabling the model to weight the wheat ear target within the receptive field when extracting features, effectively enhancing the model’s feature extraction ability and recall rate for the wheat ear target. Moreover, a P2 small target layer was added to the neck and head of the model to improve the model’s ability to detect small targets and the precision rate. Experimental results showed that on the Global Wheat Head Detection 2021 (GWHD2021) public dataset, the mean average precision of the improved model reached 92. 5%, an increase of 2. 9 percentage points compared with that of the original model. The mean average precision of the improved model on the public datasets WEDD, Spike, RGWHD, ACID, and the self-built dataset HBAUW (Hebei Agricultural University Wheat) was on average 4. 6 percentage points higher than that of the original model, fully demonstrating that this model can effectively improve the detection accuracy of wheat ears in complex backgrounds. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Convolution

Controlled terms: Complex networks? - ?Extraction? - ?Feature extraction

Uncontrolled terms: Back-bone network? - ?CBAM? - ?Complex background? - ?Ear recognition? - ?Public dataset? - ?Receptive field attention convolution? - ?Receptive fields? - ?Recognition methods? - ?Wheat ear recognition? - ?YOLO v8n

Classification code: 716.1 Information Theory and Signal Processing? - ?802.3 Chemical Operations? - ?1101.2 Machine Learning? - ?1105 Computer Networks

Numerical data indexing: Percentage 5.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.048

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

3. Optimization of Excitation Point Location for DEM MBD-based Panax notoginseng Separation Device

Accession number: 20254919628551

Title of translation: 基于 DEM MBD 仿真的三七分离装置激振点位置优化研究

Authors: Wu, Zhandong (1, 2); Sai, Yuxiang’ao (1, 2); Xie, Kaiting (2, 3); Zhang, Zhaoguo (1, 2); Guo, Rong (1, 2); Wang, Pulin (1, 2)

Author affiliation: (1) Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming; 650500, China; (2) Research Center on Mechanization Engineering, Chinese Medicinal Materials in Yunnan University, Kunming; 650500, China; (3) Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming; 650500, China

Corresponding author: Zhang, Zhaoguo(zzg@kust.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 264-274

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address issues such as poor soil fragmentation, severe soil clogging, and low root-soil separation efficiency in ginseng combined harvesters, a coupled simulation method was proposed based on the discrete element method and multi-body dynamics, optimized for excitation point location, to improve the design of the conveying and separation device in ginseng harvesters. The EDEM Recurdyn coupled simulation technology was used to systematically investigate the dynamic evolution law of the separation process of Panax notoginseng roots and soil. The simulation results showed that the loosening and separation of large soil clumps mainly depended on the energy input characteristics of the excitation zone, in which the excitation point position had a significant impact on the peak force and impact rhythm. Based on this, the separation efficiency of Panax notoginseng roots and soil and the maximum force peak were selected as evaluation indicators, and the excitation point position, vibration frequency, vibration amplitude, and lifting speed were selected as influencing factors to carry out a quadratic regression orthogonal rotation test to analyze and determine the optimal parameter combination for the conveying and separation device: excitation point position was 500 mm, vibration amplitude was 48 mm, vibration frequency was 3. 9 Hz, and lifting speed was 0. 73 m / s. Under this parameter combination, the root soil screening rate was 96. 11%, and the force on Panax notoginseng was 46. 05 N. To verify the reliability of the model, a bench comparison test was conducted, using a high-speed photography system to measure the bounce height and impact acceleration of the particles / rhizomes. The results showed that the relative error between the simulation and test data was 5. 43% and 5. 6%, respectively, verifying the reliability of the model. The research result can provide a reliable theoretical basis for the structural improvement and optimization of the conveyor separation device of the subsequent Panax notoginseng combined harvester. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: High speed photography

Controlled terms: Conveying? - ?Conveyors? - ?Discrete element methods? - ?Efficiency? - ?Finite difference method? - ?Harvesters? - ?Screening? - ?Soil testing? - ?Soils

Uncontrolled terms: Conveyor separation device? - ?Coupled simulation? - ?DEM - MBD coupled simulation? - ?Excitation points? - ?Panax notoginseng? - ?Point location? - ?Separation devices? - ?Separation efficiency? - ?Vibration frequency? - ?Vibration point

Classification code: 483.1 Soils and Soil Mechanics? - ?692.1 Conveyors? - ?742.1 Photography? - ?802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?913.1 Production Engineering? - ?941.5 Mechanical Variables Measurements? - ?1201.5 Computational Mathematics? - ?1201.9 Numerical Methods? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Force 5.00E+00N, Frequency 9.00E+00Hz, Percentage 1.10E+01%, Percentage 4.30E+01%, Percentage 6.00E+00%, Size 4.80E-02m, Size 5.00E-01m, Velocity 7.30E+01m/s

DOI: 10.6041/j.issn.1000-1298.2025.11.025

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

4. Design and Experiment of Typical Grassland Ground Image Perception and Stabilization System

Accession number: 20254919633874

Title of translation: 典型草原地面图像感知稳定系统设计与试验

Authors: Zhang, Yuzhuo (1, 2); Wang, Decheng (1); You, Yong (1); Wang, Tianyi (1)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) School of Future Science and Engineering, Soochow University, Suzhou; 215222, China

Corresponding author: Wang, Tianyi(tianyi.wang@can.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 42-53

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: An image perception stabilization system integrated with vibration compensation was designed to address the issue of image blurring and distortion caused by complex grassland terrain. Vibration tests conducted on the grassland terrain helped identify the primary direction of vibration effects, vibration displacement, and frequency control thresholds. Based on these findings, a dynamic model of the stabilization mechanism was established by using the Newton Euler method, and a fuzzy PID vibration compensation control strategy was developed, taking into account the road condition parameters. ANSYS random vibration simulations were performed to verify the strength of the mechanism, and the strain and stress distributions were found to conform to a normal distribution (95% confidence interval). Furthermore, a virtual prototype was built by using ADAMS Matlab co-simulation, confirming that the fuzzy PID control strategy effectively reduced vibration interference. On-site experiments conducted in Inner Mongolia demonstrated that the system significantly improved the structural similarity index, peak signal-to-noise ratio, and normalized cross-correlation, with increases of 0. 420, 12. 8 dB, and 0. 433, respectively. The proportion of high-quality images was increased by 28. 4%, while medium and low-quality images were decreased by 17. 8% and 11. 5%, respectively. The research indicated that the proposed vibration-compensated image perception stabilization system substantially enhanced vegetation imaging quality under complex terrain conditions, providing both a theoretical foundation and technical support for the development of grassland ecological monitoring equipment. The system not only improved image stability by reducing vibration interference but also achieved notable improvements in image quality during practical applications, offering a feasible technological solution for future grassland ecological monitoring efforts. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Signal to noise ratio

Controlled terms: Ecology? - ?Image quality? - ?Landforms? - ?MATLAB? - ?Normal distribution? - ?Proportional control systems? - ?Software prototyping? - ?Stabilization? - ?Stresses? - ?Three term control systems ? - ?Vegetation? - ?Vibration analysis? - ?Vibration control

Uncontrolled terms: Control strategies? - ?Cosimulation? - ?Fuzzy-PID control? - ?Grassland vegetation? - ?Grassland vegetation perception? - ?Image perception? - ?Image perception stabilization system? - ?Random vibrations? - ?Stabilization systems? - ?Vibration compensation

Classification code: 103 Biology? - ?214.1.1 Stress and Strain? - ?481.1 Geology? - ?716.1 Information Theory and Signal Processing? - ?731.1 Control Systems? - ?731.3 Specific Variables Control? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?941.5 Mechanical Variables Measurements? - ?1106.3 Digital Signal Processing? - ?1106.3.1 Image Processing? - ?1106.5 Computer Applications? - ?1106.9 Computer Software? - ?1201.5 Computational Mathematics? - ?1202.1 Probability Theory? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Decibel 8.00E+00dB, Percentage 4.00E+00%, Percentage 5.00E+00%, Percentage 8.00E+00%, Percentage 9.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.003

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

5. Multispectral Remote Sensing Image Fusion GAN for Inverse Model of CO2 Emission Flux in Agricultural Ecosystems

Accession number: 20255019668839

Title of translation: 多光谱遥感影像融合生成式对抗网络的农田生态系统CO2 排放通量反演模型

Authors: Zhao, Wenju (1, 2); Yu, Haiying (1, 3); Li, Haolin (4); Ding, Lei (1, 2); Yang, Pengtao (1, 2)

Author affiliation: (1) School of Civil and Hydraulic Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; (2) Key Laboratory of Smart Agriculture Irrigation Equipment, Ministry of Agriculture and Rural Affairs, Lanzhou; 730050, China; (3) College of Civil Engineering, Hexi University, Zhangye; 734000, China; (4) College of Energy and Power Engineering, Beijing University of Technology, Beijing; 100124, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 621-631

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: For the difficult problems of the continuity and dynamic monitoring of CO2 emission fluxes in agricultural ecosystems, as well as the high cost associated with acquiring actual samples, focusing on greenhouse-grown tomatoes, multispectral remote sensing images were utilized to obtain vegetation indices and texture features. The research optimized the model input layer through three variable selection methods: support vector machine – recursive feature elimination (SVM – RFE), Pearson correlation coefficient (PCC) method, and grey relational analysis (GRA) . By using static chamber – gas chromatography observations of crop canopy CO2 emission fluxes as ground – truth values, the inversion models for agricultural ecosystem CO2 emission flux were constructed by employing extreme gradient boosting (XGBoost), support vector machines (SVM) and random forest (RF) algorithms. To enhance predictive performance and generalization ability of the models, it integrated generative adversarial networks (GAN) to augment the sample set. The results demonstrated that all three inversion models can achieve rapid inversion of CO2 emission fluxes and the inversion effects from high to low were XGBoost, SVM and RF. Notably, utilizing selected texture features and vegetation indices as the model input layer through variable selection significantly enhanced the inversion accuracy. Among different machine learning algorithms, the XGBoost model exhibited optimal inversion performance, with R2p ranging from 0. 565 to 0. 762, RMSEp from 0. 266 mg/ (m2·h) to 0. 861 mg/ (m2·h) and MAEp from 0. 222 mg/ (m2·h) to 0. 793 mg/ (m2·h) across test sets. Regarding variable selection methods, the model inversion performed best when variables filtered through SVR – RFE were used as input layers, with the XGBoost SVR RFE combination showing superior results. Overall, integrating GAN into nine coupled models improved their robustness and reliability, with all models having R2p/R2c above 0. 85, where the GANXGBoost – SVR – RFE inversion model demonstrated the highest robustness. Subsequently, temporal-spatial distribution maps of CO2 emission flux in agricultural ecosystems were created, revealing emission patterns throughout the entire growth period of tomatoes. This research can offer a theoretical basis for dynamic monitoring and scientific quantification of CO2 emission flux in agricultural ecosystems. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 50

Main heading: Carbon dioxide

Controlled terms: Agricultural machinery? - ?Correlation methods? - ?Crops? - ?Ecosystems? - ?Feature extraction? - ?Gas emissions? - ?Inverse problems? - ?Learning algorithms? - ?Learning systems? - ?Quality control ? - ?Support vector machines? - ?Textures? - ?Vegetation

Uncontrolled terms: Adversarial networks? - ?Agricultural ecosystem CO2 emission flux? - ?Agricultural ecosystems? - ?CO 2 emission? - ?Emissions fluxes? - ?Input layers? - ?Inverse modelling? - ?Inversion models? - ?Spectral indices? - ?Texture features

Classification code: 103 Biology? - ?214 Materials Science? - ?731.1 Control Systems? - ?804.2 Inorganic Compounds? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?913.3 Quality Assurance and Control? - ?1101.2 Machine Learning? - ?1201 Mathematics? - ?1202.2 Mathematical Statistics? - ?1502.1.1.1.1 Air Pollution Sources? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Mass 2.22E-04kg, Mass 2.66E-04kg, Mass 7.93E-04kg, Mass 8.61E-04kg

DOI: 10.6041/j.issn.1000-1298.2025.11.060

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

6. Optimization and Test of Centrifugal Fan Blade of Cotton Picker

Accession number: 20254919629808

Title of translation: 采棉机离心风机叶片优化与试验

Authors: Feng, Jing’an (1); Chen, Jing (1); Shao, Wenping (1); Wang, Lei (1); Diao, Shen’gang (1); Chen, Chao (1); Li, Yuhang (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832000, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 275-284

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The performance of the centrifugal fan in the pneumatic conveying system of the cotton picker has an important impact on its work, so it is of great significance to improve the working efficiency and performance of the centrifugal fan of the cotton picker. The mid-arc line of the prototype centrifugal fan blade was extracted, and the NACA 6410 airfoil with excellent flow characteristics was transplanted and coupled to the mid-arc line of the prototype blade to design a type of centrifugal fan blade. Combined with test data of prototype fan in centrifugal fan of pneumatic conveying system of cotton picker, the performance change of the fan was analyzed by CFD numerical calculation. Through the centrifugal fan performance test, the aerodynamic performance of the prototype fan and the optimized fan at different speeds was compared, and the improvement effect of new fan and influence of speed on the performance of optimized fan were discussed. The results showed that the optimized blade had good aerodynamic performance. The blade structure effectively suppressed the airflow disorder, ‘jet-wake’, flow separation and other phenomena inside the fan, making the airflow on the surface of the fan blade more stable, thereby improving the efficiency of the fan. Under the rated operating conditions, compared with the efficiency of 54. 2% of the prototype fan, the efficiency of the optimized centrifugal fan was significantly improved, reaching 58. 4% . This improvement showed that the performance of the fan was significantly optimized and enhanced. Compared with the prototype fan, the operating efficiency of the optimized fan at different operating conditions was improved by 3. 0 ~ 5. 3 percentage points. Especially at 4 200 r / min, the improvement of the aerodynamic performance of the fan was the most significant, and the efficiency improvement was up to 5. 3 percentage points. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Flow separation

Controlled terms: Aerodynamics? - ?Airfoils? - ?Centrifugation? - ?Conveying? - ?Cotton? - ?Efficiency? - ?Fans? - ?Fighter aircraft? - ?Pneumatic conveyors? - ?Pneumatics ? - ?Turbomachine blades? - ?Wakes

Uncontrolled terms: Aero-dynamic performance? - ?Arc lines? - ?Blade optimisation? - ?Centrifugal fans? - ?Cotton pickers? - ?Fan blades? - ?NACA 6410 airfoil? - ?Performance? - ?Performances analysis? - ?Pneumatic conveying system

Classification code: 301.1 Fluid Flow? - ?301.1.3 Aerodynamics (fluid flow)? - ?609.3 Blowers and Fans? - ?651 Aerodynamics? - ?652.1.2 Military Aircraft? - ?692.1 Conveyors? - ?802.3 Chemical Operations? - ?821.5 Agricultural Products? - ?913.1 Production Engineering? - ?1007 Turbomachinery? - ?1401.3 Pneumatics, Equipment and Machinery

Numerical data indexing: Angular velocity 3.34E+00rad/s, Percentage 2.00E+00%, Percentage 4.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.026

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

7. Evaluation of Application of Different Resistance Models in Summer Maize Evapotranspiration Simulation

Accession number: 20254919628181

Title of translation: 不同阻力模型在夏玉米蒸散模拟中的应用性评价

Authors: Hu, Jian (1); Liu, Yuelei (1); Ma, Rong (1); Zheng, Jing (2, 3); Fan, Junliang (4); Zhao, Lu (5); Jiang, Shouzheng (5)

Author affiliation: (1) College of Water Resources and Hydropower, Sichuan Agricultural University, Ya’an; 625014, China; (2) Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu; 610299, China; (3) Key Laboratory of Mountain Surface Growth and Ecological Regulation, Chinese Academy of Sciences, Chengdu; 610299, China; (4) Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Shaanxi, Yangling; 712100, China; (5) State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu; 610065, China

Corresponding author: Jiang, Shouzheng(jiangshouzheng@scu.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 658-666

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Evapotranspiration (ET), which covers the exchange of water between soil, plants and the atmosphere, is a key link in the water cycle and energy balance. Canopy resistance (rc) and stomatal resistance (rcs) are important resistance parameters that affect the accuracy of ET. Two resistance models, Katerji Perrier(KP) and Todorovic(TD) were selected to evaluate the applicability of P M model and S W model for ET simulation in summer maize fields in Northwest China. Based on the field experiment data from 2015 to 2018, the key parameters of the resistance model were calibrated and verified by least square method. The results showed that KP model performed better than TD model in P M model and S W model. The SW KP model had the highest ET simulation accuracy, with mean value of coefficient of determination R2 of 0. 816 and mean value of root mean square error (RMSE) of 0. 79 mm / d. The underestimation of ET by PM KP model in summer maize seedling stage was only 0. 86%, while the performance of SW KP model was the best at the stage of ascending, spinning and milking, with R2 ranging of 0. 718 ± 0. 071 and RMSE ranging of (0. 85 ± 0. 11) mm / d. Model sensitivity analysis showed that rc and rcs in KP model were most sensitive to changes in net radiation (Rn ) and saturated vapor pressure difference (VPD) . In the TD model, the air temperature (Ta ) was the most sensitive. When Ta changed ± 20%, rc and rcs changed more than 10% . In PM KP model, ET was the most sensitive to Rn, and the sensitivity to Ta was 19. 69% when Ta was reduced by 20% . In PM TD model, ET was the most sensitive to Ta, and ET changed by about 30% when Ta changed by ± 20% . In the S W coupling model, the sensitivity of ET to environmental variables, ranked from high to low was Rn, Ta, soil moisture content (SWC), VPD, and the change of ET exceeded 20% when Rn changed ± 20% . The KP model corrected by the least square method had higher accuracy, the PM KP model was suitable for ET simulation at seedling stage, and the SW KP model had higher accuracy for ET simulation at summer maize’s articulation stage, spinneret stage and milk ripening stage. The research result can provide a better model combination form for ET simulation at different growth stages of maize, and provide a theoretical basis for accurate calculation of water consumption and establishment of irrigation system in summer maize fields in semi-arid areas. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Mean square error

Controlled terms: Atmospheric temperature? - ?Forestry? - ?Grain (agricultural product)? - ?Plants (botany)? - ?Sensitivity analysis? - ?Soils

Uncontrolled terms: Air temperature? - ?Canopy resistance? - ?P-M model? - ?Penman-Monteith models? - ?Resistance models? - ?S-W models? - ?Shuttleworth - wallace model? - ?Stomatal resistance? - ?Summer maize? - ?Summer maize field

Classification code: 103 Biology? - ?443.1 Atmospheric Properties? - ?483.1 Soils and Soil Mechanics? - ?821.1 Woodlands and Forestry? - ?821.5 Agricultural Products? - ?1201 Mathematics? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 1.00E+01%, Percentage 2.00E+01%, Percentage 3.00E+01%, Percentage 6.90E+01%, Percentage 8.60E+01%, Size 7.90E-02m

DOI: 10.6041/j.issn.1000-1298.2025.11.064

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

8. Soil Salinity Inversion with Sentinel 1 / 2 Multi-source Remote Sensing Data

Accession number: 20254919629111

Title of translation: 协同哨兵 1 / 2 多源遥感数据的土壤含盐量反演

Authors: Huang, Yue (1); Li, Xianyue (1); Gao, Huijuan (1)

Author affiliation: (1) College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China

Corresponding author: Li, Xianyue(lixianyue80@126.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 640-650

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Rapid monitoring of soil salinization information in the region is of great significance for salinization management and ecological environmental protection. To address the issue of low inversion accuracy of soil salinity due to single data sources, indices, and algorithms, the soil salinity in the left third branch canal of the Yichang irrigation area in the Hetao region from 2022 to 2023 was focused. Sentinel 1 / 2 data was utilized to collaboratively invert soil salinity, and then obtain the image texture and polarization indices of Sentinel 1 through the water cloud model and gray-level co-occurrence matrix. Based on this, three datasets were constructed: A (salinity index), B (salinity index with image texture), and C (salinity index with image texture and polarization index). The Bayesian information criterion and all-subsets selection method were combined to select sensitive factors, and three machine learning methods were used to construct the optimal soil salinity estimation model and produce inversion maps. The results showed that, except for bands B8, B11, and B12, the surface reflectance of the soil had a weak negative correlation with soil salinity, while the vegetation index and polarization index had a highly significant positive correlation with soil salinity; the image textures of Sentinel 1 different polarization bands were highly correlated with soil salinity, particularly in the VH band. The salinity index, image texture, and polarization index were complementary in mechanism, increasing the determination coefficient from 0. 723 and 0. 771 to 0. 863 compared with that using a single or two combined factors, showing great potential in estimating soil salinity. The random forest model constructed based on the salinity index, image texture, and polarization index was the best model for monitoring soil salinity (with a determination coefficient of 0. 863, mean absolute error of 0. 17%, and root mean square error of 0. 12%), significantly improving the underestimation of low soil salinity values. The results can provide a theoretical basis for predicting soil salinity in salinized areas. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 50

Main heading: Learning systems

Controlled terms: Barium compounds? - ?Bayesian networks? - ?Ecology? - ?Environmental management? - ?Errors? - ?Image enhancement? - ?Irrigation? - ?Machine learning? - ?Polarization? - ?Quality control ? - ?Reflection? - ?Soil surveys? - ?Soils? - ?Textures? - ?Vegetation

Uncontrolled terms: Determination coefficients? - ?Image polarization? - ?Index image? - ?Machine-learning? - ?Multi-Sources? - ?Polarization indices? - ?Salinity indices? - ?Sentinel 1 / 2? - ?Sentinel-1? - ?Soil salinity

Classification code: 103 Biology? - ?214 Materials Science? - ?405.3 Surveying? - ?483.1 Soils and Soil Mechanics? - ?731.1.1 Error Handling? - ?804.2 Inorganic Compounds? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.4 Agricultural Methods? - ?913.3 Quality Assurance and Control? - ?1101.2 Machine Learning? - ?1106.3.1 Image Processing? - ?1201.5 Computational Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1301.3 Optics? - ?1501.1 Sustainable Development? - ?1502.1 Environmental Impact and Protection? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 1.20E+01%, Percentage 1.70E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.062

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

9. Rice Pest Detection Method for Insecticidal Lamps Based on Improved YOLO v10n

Accession number: 20254819604892

Title of translation: 基于改进 YOLO v10n 的虫情灯水稻害虫检测方法

Authors: Jiang, Sheng (1); Guo, Hongpei (1); Wang, Weixing (2); Ouyang, Cong (1); Yang, Shanglin (1); Ye, Yun (3, 4); Huang, Zhihong (5)

Author affiliation: (1) College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou; 510642, China; (2) Zhujiang College, South China Agricultural University, Guangzhou; 510900, China; (3) Guangzhou Hairui Intelligent Technology Co., Ltd., Guangzhou; 510700, China; (4) School of Eco-environment Technology, Guangdong Industry Polytechnic University, Guangzhou; 528225, China; (5) Information Network Center, South China Agricultural University, Guangzhou; 510642, China

Corresponding author: Huang, Zhihong(huangzh@scau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 550-559

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of low detection accuracy and slow speed in the task of detecting rice field pests under insect pest monitoring and control lamps, including small pest target size, morphological diversity, and stacked and overlapping pest clusters, a recognition method based on improved YOLO v10n (named YOLO FRP, short for YOLO five rice pest) was proposed. The bidirectional feature pyramid network (BiFPN) was introduced to improve the original feature pyramid network (FPN), reducing parameters for easier deployment while leveraging bidirectional feature propagation to enhance the model’s fusion of low-level and high-level features. Meanwhile, a P2 small-target layer was further integrated into the BiFPN to strengthen multi-scale feature fusion, boosting stronger contextual awareness of diverse target sizes. A lightweight enhancement module, C2f_Star, was designed to replace the shallow C2f modules in the backbone network to improve multi-scale detection accuracy and computational efficiency. Concurrently, the C2fCIB module from YOLO v10s was transplanted into the backbone to replace the deep C2f modules to reduce computational complexity while enhancing precision. The experimental results demonstrated that the improved model achieved mAP@ 0. 5 of 82. 1% and recall rate of 79. 5%, with parameter count of 1. 64 × 106 and model size of 4. 3 MB. To further achieve high real-time detection and lightweight deployment of the model, a joint distillation pruning strategy was adopted. The improved YOLO v10s model, which was enhanced with the EMA attention mechanism and the C2f_DySnakeConv dynamic snake-shaped convolution module, was used as the teacher model. L2 regularization distillation was applied to enhance the performance of the YOLO FRP model, and a layer-adaptive magnitude-based pruning strategy was further adopted to reduce the model’s parameter count. Compared with the YOLO v10n model, the pruned and distilled model achieved 2. 6 percentage points higher mAP@ 0. 5 and 2. 7 percentage points higher recall rate, and its frame rate was raised by 158 f / s; its parameter count and model size were decreased by 65. 64% and 56. 36% respectively. Comparative experiments on the self-built dataset confirmed the effectiveness of the proposed improvements. The YOLO FRP model exhibited superior detection accuracy and speed for five rice pest species under insect-attracting lamps, which validated that the distillation pruning strategy could further compress the model size without accuracy loss and significantly improve the inference speed, providing technical reference for balancing accuracy and speed in real-time agricultural pest monitoring. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Object detection

Controlled terms: Complex networks? - ?Computational efficiency? - ?Convolution? - ?Cost effectiveness? - ?Distillation? - ?Feature extraction? - ?Insect control? - ?Learning systems? - ?Object recognition? - ?Scattering parameters

Uncontrolled terms: Detection accuracy? - ?Feature enhancement? - ?Feature pyramid? - ?Knowledge distillation? - ?Lightweight? - ?Model size? - ?Objects detection? - ?Pruning strategy? - ?Pyramid network? - ?Rice pests

Classification code: 703.1.1 Electric Network Analysis? - ?716.1 Information Theory and Signal Processing? - ?802.3 Chemical Operations? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?911.2 Industrial Economics? - ?1101.2 Machine Learning? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1105 Computer Networks? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 1.00E00%, Percentage 3.60E+01%, Percentage 5.00E+00%, Percentage 6.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.053

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

10. Maize Lodging Detection in UAV Remote Sensing Images Based on DSM and Improved PIDNet

Accession number: 20254819604910

Title of translation: 基于数字表面模型和改进 PIDNet 的玉米倒伏无人机遥感监测研究

Authors: Li, Jiahao (1, 2); Liu, Kaidong (1, 2); Chai, Zikai (1, 2); Ning, Jifeng (3, 4); Yang, Shuqin (1, 2)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Shaanxi, Yangling; 712100, China; (3) College of Information Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (4) Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Shaanxi, Yangling; 712100, China

Corresponding author: Yang, Shuqin(yangshuqin1978@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 378-386

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Lodging significantly affects the growth, development, and yield of maize. Timely and effective detection of lodging locations and identification of lodging areas in cornfields is crucial for production remediation, breeding of improved varieties, and disaster assessment. A multi-growth stage maize lodging detection method was proposed based on UAV remote sensing by integrating the improved semantic segmentation model PIDNet with DSM containing maize height information. Firstly, UAV remote sensing images of maize at the silking, milk, and dough stages were fused with visible light images and DSM images to serve as the input layer for the PIDNet model, enhancing the features of lodging areas. Secondly, an efficient multi-scale attention (EMA) module was added to the integrative branch of PIDNet to improve the network’s ability to aggregate contextual information, detail parsing, and boundary detection, thereby enhancing the model’s generalization capability, which resulted in the EMA PIDNet maize lodging detection model. Experimental results showed that the proposed EMA PIDNet model integrating DSM achieved mean pixel accuracy (mPA) and mean intersection over union (mIoU) of 91. 83% and 83. 94% at the silking stage, 91. 66% and 82. 77% at the milk stage, and 90. 84% and 82. 64% at the dough stage, respectively. Compared with representative models such as U Net, PSPNet, DeepLabv3 +, and UHRNet, the proposed EMA PIDNet model demonstrated superior performance in detecting maize lodging areas across all three growth stages. Ablation experiments confirmed the effectiveness of integrating DSM height information and adding the EMA module to the PIDNet model. The research result indicated that the method of integrating DSM height information can more accurately detect maize lodging areas, and the EMA module enhanced the detection capability of maize lodging across multiple growth stages, providing effective support for using UAV remote sensing technology to detect maize lodging. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Image enhancement

Controlled terms: Aircraft detection? - ?Disaster prevention? - ?Grain (agricultural product)? - ?Remote sensing? - ?Semantic Segmentation? - ?Semantics? - ?Unmanned aerial vehicles (UAV)

Uncontrolled terms: Digital surface models? - ?Growth stages? - ?Growth yield? - ?Image-based? - ?Maize lodging? - ?Multi-scales? - ?PIDNet? - ?Remote sensing images? - ?Semantic segmentation? - ?UAV remote sensing

Classification code: 435.2 Tracking and Positioning? - ?652.1 Aircraft? - ?716.2 Radar Systems and Equipment? - ?731.1 Control Systems? - ?821.5 Agricultural Products? - ?903.2 Information Dissemination? - ?914.1 Accidents and Accident Prevention? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 6.40E+01%, Percentage 6.60E+01%, Percentage 7.70E+01%, Percentage 8.30E+01%, Percentage 8.40E+01%, Percentage 9.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.036

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

11. Effects of Subsoiling Combined with Nitrogen Application on Root Growth and Nitrogen Utilization Characteristics of Rainfed Potato

Accession number: 20254919628515

Title of translation: 深松施氮对旱作马铃薯根系生长与氮素利用特征的影响

Authors: Li, Rong (1); Liu, Tingting (1); Ma, Xiaoming (2); Zhang, Yuewei (1); Zhang, Xu (1); Hou, Xianqing (1)

Author affiliation: (1) School of Agriculture, Ningxia University, Yinchuan; 750021, China; (2) Agricultural Technology and Agricultural Mechanization Promotion Service Center of Qingtongxia, Qingtongxia; 751600, China

Corresponding author: Hou, Xianqing(houxianqing1981@126.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 696-707

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A field experiment was conducted over two consecutive years (2021—2022) to investigate the effects of subsoiling combined with nitrogen application on soil moisture, nitrogen content, potato root length, root activity, nitrogen accumulation in various organs, nitrogen use efficiency, and yield. The experimental design comprised four tillage treatments: conventional tillage at 20 cm depth (F20), and subsoiling at 30 cm (S30), 40 cm (S40), and 50 cm (S50) depths, combined with four nitrogen application rates: 0 kg/ hm2(N0), 90 kg/ hm2(N90), 180 kg/ hm2(N180), and 270 kg/ hm2(N270). The results showed that increasing subsoiling depth compared with conventional tillage at 20 cm significantly enhanced soil water storage and moisture content during the period of 60 ~ 120 days after potato sowing, with the best effect observed under the S50 treatment. Conversely, an increase in nitrogen application rate reduced soil water storage and moisture content. The combination of subsoiling with nitrogen application significantly increased soil total nitrogen and alkali-hydrolyzable nitrogen content, and the S30 × N270 treatment in 2021 and the S40 × N270 and S50 × N270 treatments in 2022 showed the highest increases. Potato root length and root vigor were significantly influenced by the interaction of tillage depth and nitrogen application. In 2021, the S30 × N180 treatment exhibited the best performance, while in 2022, the S40 × N180 treatment was the highest. The combination of subsoiling and nitrogen application significantly affected nitrogen accumulation. Under the same tillage depth, the N180 treatment showed the highest effect, while under the same nitrogen application rate, subsoiling at 30 ~ 40 cm depth achieved the highest performance. Potato yield was significantly influenced by the interaction of tillage depth and nitrogen application. In 2021, the S30 × N180 treatment resulted in the highest yield, while in 2022, the S40 × N180 treatment was optimal. Through fitting the relationship between tillage depth and nitrogen application rate with potato yield, it was found that a tillage depth of 35. 1 ~ 37. 2 cm combined with a nitrogen application rate of 151. 5 ~ 160. 7 kg / hm2 could achieve synergistic improvement in both high yield of potato and nitrogen use efficiency. The combination of subsoiling and nitrogen application effectively improved agronomic nitrogen use efficiency, nitrogen partial productivity, and nitrogen use efficiency. The optimal effect was observed with subsoiling at a depth of 30 ~ 40 cm combined with a nitrogen application rate of 90 ~ 180 kg / hm2 . In conclusion, the combination of subsoiling and nitrogen application significantly improved the soil water and nitrogen environment in dryland areas of southern Ningxia, promoted potato root growth, enhanced nitrogen use efficiency, and increased potato yield. It was recommended that subsoiling depth at 35 ~ 40 cm combined with the nitrogen application rate of 151 ~ 180 kg / hm2 can be adopted as an integrated tillage with nitrogen management strategy for potato production and efficient nitrogen utilization in the dryland areas of southern Ningxia. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Moisture determination

Controlled terms: Agricultural machinery? - ?Cultivation? - ?Design of experiments? - ?Efficiency? - ?Nitrogen? - ?Plants (botany)? - ?Soil pollution? - ?Tillage

Uncontrolled terms: Nitrogen application rates? - ?Nitrogen utilization? - ?Nitrogen-use efficiency? - ?Potato? - ?Potato yield? - ?Root activities? - ?Root growth? - ?Soil nitrogen? - ?Soil water? - ?Subsoiling combined with nitrogen application

Classification code: 103 Biology? - ?483.1 Soils and Soil Mechanics? - ?804 Chemical Products? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?901.3 Engineering Research? - ?904 Design? - ?913.1 Production Engineering? - ?941.6 Moisture Measurements? - ?1502.1.1.3 Soil Pollution

Numerical data indexing: Age 1.644E-01yr to 3.288E-01yr, Mass 0.00E00kg, Mass 1.51E+02kg to 1.80E+02kg, Mass 1.80E+02kg, Mass 2.70E+02kg, Mass 7.00E+00kg, Mass 9.00E+01kg to 1.80E+02kg, Mass 9.00E+01kg, Size 2.00E-01m, Size 2.00E-02m, Size 3.00E-01m, Size 3.00E-01m to 4.00E-01m, Size 3.50E-01m to 4.00E-01m, Size 4.00E-01m, Size 5.00E-01m

DOI: 10.6041/j.issn.1000-1298.2025.11.068

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

12. Sugarcane Bud Detection Method Based on YOLO 11n-EWL

Accession number: 20254819600410

Title of translation: 基于 YOLO 11n-EWL 的甘蔗芽检测方法

Authors: Li, Shangping (1, 2); Liu, Jianju (1, 3); Wang, Heng (1, 3); Wang, Baoyin (1, 3); Li, Kaihua (1, 3)

Author affiliation: (1) School of Physics and Electronic Information, Guangxi Minzu University, Nanning; 530006, China; (2) Province and Ministry Co-sponsored Collaborative Innovation Center of Cane Sugar Industry, Nanning; 530006, China; (3) Key Laboratory of Intelligent Unmanned System and Intelligent Equipment, Nanning; 530006, China

Corresponding author: Li, Kaihua(1023604966@qq.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 471-479

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of low efficiency and high labor intensity in manual bud selection during sugarcane seed preparation, it was focused on two key aspects: model detection accuracy and dynamic recognition rate. A lightweight model based on an improved YOLO 11n, named YOLO 11n-EWL, was proposed to enhance the detection accuracy of sugarcane buds in images, and systematic experiments were conducted to optimize dynamic recognition performance. The C3k2 module was improved by using the Sobel operator; by incorporating a Sobel Conv branch that extracted edge information, the detection accuracy for sugarcane buds was enhanced. Wavelet pooling was introduced to optimize the pooling method in the baseline model (YOLO 11n), improving detection accuracy while reducing model complexity. Based on the lightweight shared convolutional detection (LSCD) head, a detail-enhanced convolution (DEC) was applied to design a more lightweight detection head named lightweight shared convolutional detection detail-enhanced convolution (LSCDECD), further reducing the model’s floating-point operations (FLOPs) and parameter count, making it more suitable for deployment on edge systems. Results showed that the improved model (YOLO 11n-EWL) achieved mAP@ 0. 5, precision, and recall of 99. 4%, 99. 2%, and 98. 3%, respectively, representing improvements of 4. 7, 3. 7, and 12. 6 percentage points over the baseline model (YOLO 11n). The FLOPs and parameter count were reduced by 25. 8% and 31. 7%, respectively, and the frame rate was increased by 14 f/ s. Under conditions of conveyor belt speed, illumination intensity, and exposure time set at 18. 84 cm / s, 16 520 lx, and 1 000 μs, respectively, the dynamic detection and recognition rate for sugarcane buds reached 99. 0%, with average of 4 522 sugarcane segments identified per hour. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Digital arithmetic

Controlled terms: Belt conveyors? - ?Convolution? - ?Edge detection? - ?Molasses? - ?Plants (botany)? - ?Sugar cane

Uncontrolled terms: Baseline models? - ?Detection accuracy? - ?Detection methods? - ?Dynamic detection? - ?Dynamic recognition? - ?Floating point operations? - ?Floating point parameters? - ?Lightweight? - ?Sugarcane bud recognition? - ?YOLO 11n

Classification code: 103 Biology? - ?692.1 Conveyors? - ?716.1 Information Theory and Signal Processing? - ?821.5 Agricultural Products? - ?822.3 Food Products? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Illuminance 5.20E+02lx, Percentage 0.00E00%, Percentage 2.00E+00%, Percentage 3.00E+00%, Percentage 4.00E+00%, Percentage 7.00E+00%, Percentage 8.00E+00%, Time 0.00E00s, Velocity 8.40E-01m/s

DOI: 10.6041/j.issn.1000-1298.2025.11.045

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

13. TuoA Robot Design Methodology Based on Boundary Specification and Topological Approximation

Accession number: 20254919628433

Title of translation: 基于边界规范与拓扑近似的 TuoA 机器人设计方法

Authors: Li, Yigeng (1); Zhou, Yulin (1)

Author affiliation: (1) School of Mechanical Engineering, Yanshan University, Qinhuangdao; 066004, China

Corresponding author: Zhou, Yulin(zyl@ysu.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 726-735

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In addressing the critical demand for the efficient assembly of truss units in the in-orbit manufacturing of space solar power stations, the research was cantered on the challenges posed by the limited integration of robot functions and the absence of sufficient flexibility. A novel three-branch TuoA robot that integrated walking, grasping, and manipulation functions was proposed. This robot enabled the switching of multiple motion modes among different branches, facilitating efficient and flexible in-orbit assembly. Additionally, the approach involved conducting a kinematic analysis and scale synthesis of the robot based on the D H method. The methodology employed was predicated on the D H method, which was utilized to conduct a thorough analysis of the robot’s kinematics and synthesize the scale. The objective was to address the challenges posed by the low efficiency of lightweight design, which was attributed to the frequent topology optimization and the suboptimal manufacturing process involved in constructing the stiffness and weight mapping model. To address these challenges, a novel lightweight design method was proposed. This method was based on boundary specification and topology approximation, with the aim of facilitating the rapid generation of a topology with the same mass retention ratio and force conditions. This approach was expected to enhance the design efficiency of robots by eliminating the need for repeated iterations and post-processing of the topology optimization process. This approach was shown to significantly enhance the efficiency of robot design. To illustrate this point, the TuoA robot was used as an example to carry out case validation and analysis. The process and results showed that each kind of connecting rod of the robot needed only three topology optimizations to construct a stiffness and weight mapping model with high fitting accuracy. The material distribution of the structure obtained from the topology optimization was more reasonable and free of burrs. Furthermore, the weight of the robot was reduced by 17. 4% by the global optimization under the condition of meeting the requirement of stiffness. The research result can provide a crucial reference point for the design of truss cell in-orbit assembly robot configuration. Furthermore, it established a substantial theoretical foundation for the lightweight design of other robots with analogous functionality. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Machine design

Controlled terms: Global optimization? - ?Industrial research? - ?Kinematics? - ?Mapping? - ?Robotic assembly? - ?Shape optimization? - ?Solar energy? - ?Space applications? - ?Specifications? - ?Stiffness ? - ?Topology? - ?Trusses

Uncontrolled terms: Boundary specification? - ?D-H method? - ?Design Methodology? - ?In-orbit? - ?Lightweight design? - ?Mapping modeling? - ?Robot designs? - ?Topological approximation? - ?Topology optimisation? - ?Tuoa robot

Classification code: 214 Materials Science? - ?405.3 Surveying? - ?408.1 Structural Members and Shapes? - ?601 Mechanical Design? - ?603.1 Machine Tool Accessories? - ?656 Space Flight and Research? - ?731.6 Robot Applications? - ?901.3 Engineering Research? - ?902.2 Codes and Standards? - ?904 Design? - ?912.1 Industrial Engineering? - ?1008.4 Solar Energy Conversion and Power Generation? - ?1201.2 Calculus and Analysis? - ?1201.7 Optimization Techniques? - ?1201.14 Geometry and Topology? - ?1301.1.1 Mechanics? - ?1302.1.1 Solar Energy and Phenomena

Numerical data indexing: Percentage 4.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.071

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

14. Model Construction and Test of High-speed Directional Valve-controlled Digital Hydraulic Cylinder

Accession number: 20254919628281

Title of translation: 高速换向阀控制数字液压缸模型构建与试验

Authors: Li, Yuesong (1); Meng, Long (1); Shi, Nao (1)

Author affiliation: (1) School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang; 471003, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 736-744

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Digital hydraulic cylinders have the advantages of simple structure, good stability, high linearity and high positioning accuracy, and have great application prospects in the field of industry and agriculture. However, at present, the traditional digital hydraulic cylinder relies on mechanical feedback for position positioning. A high-speed directional valve was proposed to control the digital hydraulic cylinder to get rid of mechanical feedback. Firstly, the components and working principles of the system were introduced, and the relationship between the input pulse signal and the system flow was analyzed. Secondly, simulation modeling was carried out, and the pulse generation strategy was used to control the directional valve in the simulation, and the pulse displacement equivalent under the pulse control of different duty cycle ratios of the system was obtained, and then the different pulse displacement equivalents were linearly combined, and the sine signal and square wave signal were simulated and tracked. Finally, the test system was built according to the system principle, and the average error of the system tracking the sine curve was 0. 99 mm. When the system tracked the square wave curve, the error of the square wave rising and stabilizing stage was - 0. 07 mm, and the error of the square wave falling stage was - 0. 2 mm. The research on the high-speed directional valve-controled digital hydraulic cylinder system provided an exploration idea for the valve-controlled cylinder to improve the position accuracy of the open-loop system. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Errors

Controlled terms: Agriculture? - ?Cylinders (shapes)? - ?Displacement control? - ?Feedback? - ?Hydraulic machinery? - ?Position control? - ?Valves (mechanical)

Uncontrolled terms: Digital hydraulic cylinder? - ?Digital hydraulics? - ?High Speed? - ?High-speed directional valve? - ?Hydraulic cylinders? - ?Mechanical? - ?Model construction? - ?Pulse control? - ?Pulse displacement equivalent? - ?Square-wave

Classification code: 408.1 Structural Members and Shapes? - ?601.2 Machine Components? - ?731 Automatic Control Principles and Applications? - ?731.1.1 Error Handling? - ?731.3 Specific Variables Control? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?941.5 Mechanical Variables Measurements? - ?1401.2 Hydraulic Equipment and Machinery

Numerical data indexing: Size 2.00E-03m, Size 7.00E-03m, Size 9.90E-02m

DOI: 10.6041/j.issn.1000-1298.2025.11.072

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

15. Spatial Differentiation and Driving Mechanism of Conversion of Cultivated Land to Orchards in Hilly and Mountainous Areas

Accession number: 20254919628495

Title of translation: 丘陵山区耕地”林果化” 空间分异与驱动机制研究

Authors: Lin, Jianping (1); Huang, Kun (1); Deng, Aizhen (2); Xu, Ziying (3); Chen, Jinqi (4); Yu, Wenhui (1); Wu, Chunmei (1); Zhang, Peiyi (1); Chen, Yonglin (1)

Author affiliation: (1) School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou; 341000, China; (2) Jiangxi College of Applied Technology, Ganzhou; 341000, China; (3) School of Public Administration, Jianxi University of Finance and Economics, Nanchang; 330045, China; (4) College of Land Resources, Environment and Engineering, Jiangxi Agricultural University, Nanchang; 330045, China

Corresponding author: Chen, Yonglin(gnsycyl@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 359-368

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Revealing the spatial differentiation characteristics and driving factors of conversion of cultivated land to orchards is of great significance for scientific management of cultivated land and stable grain production. Taking Anyuan County, Ganzhou City, Jiangxi Province as the research area, totally 20 indicators were selected from five aspects: topography, resource endowment, location conditions, social economy and regional policy, and the index system of correlation factors of conversion of cultivated land to orchards was systematically constructed. The spatial econometric model was used to study the spatial differentiation and driving factors of conversion of cultivated land to orchards. The results showed that from 2009 to 2022, the total area of conversion of cultivated land to orchards in Anyuan County was 4 693. 81 hm2, and the rate of conversion of cultivated land to orchards was 20. 98% . Among them, the area of cultivated land converted to garden land and forest land was 1 497. 04 hm2 and 3 196. 77 hm2, accounting for 6. 69% and 14. 29% of the rate of conversion of cultivated land to orchards, respectively. Spatial clustering analysis showed that the high-value clustering areas of conversion of cultivated land to orchards were mainly concentrated around the county and Anyuan County. Due to the dense population around the county, the demand for forest and fruit products was driven. The surrounding areas of the county were mainly hilly and mountainous areas. Natural conditions and geographical characteristics were suitable for the development of forest and fruit industry. In addition, policy support jointly promoted the problem of conversion of cultivated land to orchards. The results of the spatial econometric model analysis showed that the conversion of cultivated land to orchards was the result of the joint action of multiple factors such as natural resources, social economy and regional policies. The spatial heterogeneity of conversion of cultivated land to orchards was deeply studied, and the spatial and temporal distribution pattern and driving mechanism of conversion of cultivated land to orchards in hilly areas of southern China were systematically clarified, which can improve scientific cognition, rationally regulate conversion of cultivated land to orchards, and ensure national food security. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Orchards

Controlled terms: Agribusiness? - ?Agricultural economics? - ?Economics? - ?Forestry? - ?Fruits? - ?Land use? - ?Lettuce? - ?Natural environment? - ?Regional planning? - ?Spatial variables measurement

Uncontrolled terms: Conversion of cultivated land to orchard? - ?Cultivated lands? - ?Driving factors? - ?Driving mechanism? - ?Hilly? - ?Hilly and mountainous areas? - ?Mountainous area? - ?Social economy? - ?Spatial differentiation? - ?Spatial econometric models

Classification code: 403 Urban and Regional Planning and Development? - ?403.2 Regional Planning and Development? - ?821.1 Woodlands and Forestry? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?911.2 Industrial Economics? - ?941.5 Mechanical Variables Measurements? - ?971 Social Sciences? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 2.90E+01%, Percentage 6.90E+01%, Percentage 9.80E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.034

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

16. Estimation Model of Soybean Chlorophyll Content Based on UAV Texture Index

Accession number: 20254919628197

Title of translation: 基于无人机纹理指数的大豆叶绿素含量估算模型

Authors: Lu, Xingxing (1); Xiang, Youzhen (1); Li, Zhijun (1); Zhang, Zhitao (1); Chen, Junying (1); Zhang, Fucang (1)

Author affiliation: (1) Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Shaanxi, Yangling; 712100, China

Corresponding author: Xiang, Youzhen(youzhenxiang@nwsuaf.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 632-639

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Efficient acquisition of crop chlorophyll content is of great significance to agricultural production, as it can provide farmers with precise data foundation for implementing timely and effective field management strategies, thereby maximizing crop growth efficiency and yield. Unmanned aerial vehicle (UAV) multispectral technology was employed, and through two consecutive years of field experiments (2021—2022), soybean leaf SPAD values and corresponding UAV multispectral images were collected to establish combinations of canopy texture features and randomly extracted texture indices. By analyzing the correlation between these parameters and soybean leaf SPAD values, parameters with significant correlation coefficients (P 2 ) was 0. 883, the root mean square error (RMSE) was 0. 886, and the mean relative error (MRE) was 1. 638% . The findings can lay a foundation for UAV multispectral monitoring of chlorophyll content in soybean leaves and provide a basis for the rapid assessment of crop growth status. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Unmanned aerial vehicles (UAV)

Controlled terms: Agricultural machinery? - ?Antennas? - ?Backpropagation? - ?Chlorophyll? - ?Crops? - ?Forestry? - ?Image texture? - ?Learning systems? - ?Mean square error? - ?Neural networks ? - ?Textures

Uncontrolled terms: Aerial vehicle? - ?Chlorophyll contents? - ?Correlation coefficient? - ?Crop growth? - ?Estimation models? - ?Multi-spectral? - ?Soybean? - ?Texture features? - ?Texture index? - ?Unmanned aerial vehicle

Classification code: 101.1 Biomedical Engineering? - ?214 Materials Science? - ?652.1 Aircraft? - ?716.5.1 Antennas? - ?804.1 Organic Compounds? - ?821.1 Woodlands and Forestry? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1106.3.1 Image Processing? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 6.38E+02%

DOI: 10.6041/j.issn.1000-1298.2025.11.061

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

17. Estimation Model of Forest Feature Parameters and Its Interpretability Based on UAV and Deep Learning

Accession number: 20254919628653

Title of translation: 基于无人机与深度学习的森林特征参数估测模型构建与可解释性研究

Authors: Sun, Zhao (1); Xie, Yunhong (2); Ding, Zhidan (2); Li, Rui (2); Tan, Jun (1); Yuan, Xin (1)

Author affiliation: (1) Civil-Military Integration Center of China Geological Survey, Chengdu; 610036, China; (2) State Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing; 100083, China

Corresponding author: Yuan, Xin(502840714@qq.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 397-407

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Forest structural parameters are critical indicators for assessing forest ecosystem growth conditions. To achieve efficient and accurate estimation of these parameters, a method that integrated unmanned aerial vehicle (UAV) technology with deep neural network (DNN) models was explored, and the interpretability of model predictions was further enhanced by using the shapley additive explanations (SHAP) approach. High-resolution UAV imagery was employed as the primary data source, and the DNN model was used to estimate key forest parameters, including average diameter at breast height (AD), stand basal area (BA), Lorey’s tree height (HL), and above-ground biomass of sample plots (AGB). A multi-level DNN model was designed to process and analyze the imagery, enabling parameter prediction. In addition, the SHAP method was applied to interpret DNN outputs, thereby clarifying the contribution of each feature to the predictions. The DNN model leveraged both two-dimensional spectral features and three-dimensional point cloud features extracted from digital aerial photographs (DAP) for forest parameter estimation. For BA and AGB estimation, model performance ranked as follows: DOM + DAP point cloud, DOM, DAP point cloud. The optimal models achieved mean R2 values of 0. 743 8 for BA and 0. 776 2 for AGB. For AD and HL estimation, performance ranked as DOM + DAP point cloud, DAP point cloud, DOM, with optimal mean R2 values of 0. 613 3 for AD and 0. 727 6 for HL. SHAP values revealed the relative contribution of each feature, showing that the coefficient of variation of tree height consistently played a key role across models, while point cloud height variables provided stronger explanatory power for forest parameters. Overall, the DNN model demonstrated high estimation accuracy, while the SHAP method enhanced interpretability of the results and emphasized the importance of point cloud variables in the predictive framework. These findings indicated that the integration of modern remote sensing technology with advanced machine learning methods provided essential technical support for the application of UAV-based digital aerial photogrammetry in forest resource inventory and monitoring. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Forecasting

Controlled terms: Additives? - ?Aerial photography? - ?Agricultural machinery? - ?Antennas? - ?Deep neural networks? - ?Ecosystems? - ?Forestry? - ?Image analysis? - ?Image enhancement? - ?Learning algorithms ? - ?Learning systems? - ?Parameter estimation? - ?Prediction models? - ?Remote sensing? - ?Stereo image processing? - ?Stereo vision? - ?Unmanned aerial vehicles (UAV)

Uncontrolled terms: Deep learning? - ?Feature parameters? - ?Forest feature parameter? - ?Interpretability? - ?Multi-view stereo? - ?Multi-view stereo vision technology? - ?SFM algorithm? - ?Shapley? - ?Shapley additive explanation interpretability analyze? - ?Vision technology

Classification code: 652.1 Aircraft? - ?716.5.1 Antennas? - ?731.1 Control Systems? - ?731.6 Robot Applications? - ?742.1 Photography? - ?803 Chemical Agents and Basic Industrial Chemicals? - ?821.1 Woodlands and Forestry? - ?821.2 Agricultural Machinery and Equipment? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1106.3.1 Image Processing? - ?1201 Mathematics? - ?1202 Statistical Methods? - ?1502.2 Ecology and Ecosystems

DOI: 10.6041/j.issn.1000-1298.2025.11.038

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

18. Rapid Detection of PC Content in Artificial Soybean Crude Oil Based on Photoelectric Chemical Biosensor

Accession number: 20254919628137

Title of translation: 基于光电化学生物传感器的人工大豆毛油中PC 含量快速检测研究

Authors: Wang, Ning (1); Li, Lin (1); Luo, Shunian (2); Wang, Liqi (1, 3); Wang, Weining (4); Yu, Dianyu (4)

Author affiliation: (1) College of Food Engineering, Harbin University of Commerce, Harbin; 150028, China; (2) Jiusan Food Co., Ltd., Harbin; 150010, China; (3) College of Computer and Information Engineering, Harbin University of Commerce, Harbin; 150028, China; (4) College of Food Science, Northeast Agricultural University, Harbin; 150030, China

Corresponding author: Wang, Liqi(hsdwlq@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 708-715 and 725

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to realize real-time monitoring of phospholipid content in soybean oil degumming process, a real-time detection system for phosphatidylcholine (PC) content in artificial soybean crude oil was developed based on photoelectric chemical biosensor. The result showed that SnO2 nanoparticles (SnO2 NPs) / Polythionine (PTh) / Choline oxidase (ChOx) (SnO2 NPs/ PTh/ ChOx) was successfully combined on indium tin oxide (ITO) electrode, and the composite electrode could produce good photocurrent signal for PC content in artificial soybean crude oil. The maximum photocurrent intensity was 4. 60 μA. In addition, the photocurrent signals of 100 artificial soybean crude oil samples with different PC content were collected by electrochemical workstation, and the data of 100 samples were processed by wavelet transform denoising and partial least squares regression model (PLSR). The coefficient of determination R2 of the test set model was 0. 89, the root mean square error of prediction (RMSEP) was 24. 98 mg/ L, and the relative standard deviation (RSD) was 0. 13%, indicating that the model was stable and reliable. A real-time detection system for PC content in artificial soybean crude oil was developed by using LabVIEW and its performance was tested. The results showed that the RSD of the system was less than 4. 00%, which was similar to the detection result of the electrochemical workstation, and the detection step was effectively simplified. In conclusion, the research result provided some theoretical support for real-time monitoring of residual phosphorus in soybean oil during oil degumming in industrial field. These findings can help optimize the extraction and refining process of soybean oil and improve production efficiency, which had important implications for its application in the food and industrial sectors. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Tin oxides

Controlled terms: Chemical detection? - ?Crude oil? - ?Degumming? - ?Electrochemical electrodes? - ?Industrial research? - ?Least squares approximations? - ?Mean square error? - ?Petroleum refining? - ?Phospholipids? - ?Photocurrents ? - ?Photoelectrochemical cells? - ?Platinum? - ?Platinum compounds? - ?Regression analysis? - ?Soybean oil? - ?Wavelet transforms

Uncontrolled terms: Artificial soybean crude oil? - ?Chemical biosensors? - ?Detection system? - ?Oil degumming? - ?Phosphatidyl choline? - ?Photoelectric chemicals? - ?Photoelectrochemical biosensor? - ?Photoelectrochemicals? - ?Real time monitoring? - ?Regression modelling

Classification code: 202.7.1.3 Platinum and Alloys? - ?512.1 Petroleum Deposits? - ?513 Petroleum Refining and Refineries? - ?701.1 Electricity: Basic Concepts and Phenomena? - ?702.1 Electric Batteries? - ?704 Electric Components and Equipment? - ?741.1 Light/Optics? - ?801.3.1 Electrochemistry? - ?802 Chemical Apparatus and Plants; Unit Operations; Unit Processes? - ?804.1 Organic Compounds? - ?804.2 Inorganic Compounds? - ?822.2 Food Processing Operations? - ?822.3 Food Products? - ?901.3 Engineering Research? - ?912.1 Industrial Engineering? - ?1201.3 Mathematical Transformations? - ?1201.7 Optimization Techniques? - ?1202.2 Mathematical Statistics

Numerical data indexing: Electric current 6.00E-05A, Mass density 9.80E-02kg/m3, Percentage 0.00E00%, Percentage 1.30E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.069

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

19. Lightweight Detection Method of Rice Panicles Based on CBLP YOLO 11n

Accession number: 20254819604922

Title of translation: 基于 CBLP YOLO 11n 的无人机稻穗轻量化检测方法

Authors: Wang, Xue (1); Gao, Ya (1); Tao, Guixiang (1); Ma, Tiemin (1); Zhang, Nan (1); Xu, Shanxiang (1); Yu, Qing (2)

Author affiliation: (1) College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing; 163319, China; (2) Agricultural Technology Extension Service Center, Ning’an Dongjing Town, Mudanjiang; 157421, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 461-470

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Rice is a globally important food crop and accurate counting of rice panicles is crucial for estimating rice production. However, in actual production scenarios, due to reasons such as complex paddy field environment, diversity of rice varieties, and their panicle morphological features, existing detection methods have insufficient accuracy and a large number of model parameters. To this end, a lightweight rice panicles detection model CBLP YOLO 11n was proposed. Firstly, in Backbone, the original C3k2 module was replaced with C3k2 CFCGLU in order to enhance the model’s feature extraction and expression ability for rice panicles. Secondly, bidirectional feature pyramid network (BiFPN) was used to achieve multi-scale feature information fusion. By means of skip connections and deletion of redundant nodes, the recognition accuracy of rice panicles was improved while effectively reducing the computational complexity of the model. Then a lightweight detail-enhanced shared detection head (LDSDH) was designed and the complexity of detection head was reduced through shared convolution. Finally, the original CIoU loss function was replaced by Powerful IoUv2 loss function to accelerate the convergence speed of the model and optimize positioning accuracy of the model for rice panicles. The experimental results showed that the detection precision of the CBLP YOLO 11n model was 88. 2%, the recall was 87. 9% and the mean average precision was 93. 9% . Compared with YOLO 11n, the CBLP YOLO 11n model showed improvements in the precision, recall, mAP by 1. 9, 1. 1 and 1. 3 percentage points, respectively. Meanwhile, the CBLP YOLO 11n reduced the parameter quantity by 23. 7% and the computational quantity by 40. 6% . Compared with other mainstream models, the CBLP YOLO 11n model had the highest average detection accuracy and the smallest memory usage, which was only 3. 78 MB. The proposed model realized accurate identification of rice panicles and can be deployed in resource-constrained devices such as drones, providing technical support for identification and counting of rice panicles in complex field backgrounds. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Complex networks

Controlled terms: Crops? - ?Feature extraction? - ?Information fusion

Uncontrolled terms: Count of rice panicle? - ?Detection methods? - ?Features fusions? - ?Food crops? - ?Lightweight model? - ?Loss functions? - ?Multi-scale feature fusion? - ?Multi-scale features? - ?UAV image? - ?YOLO 11n

Classification code: 821.5 Agricultural Products? - ?903.1 Information Sources and Analysis? - ?1101.2 Machine Learning? - ?1105 Computer Networks

Numerical data indexing: Percentage 2.00E+00%, Percentage 6.00E+00%, Percentage 7.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.044

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

20. Key Point Recognition and Classification Methods of Allium chinense Based on BMR - YOLO 11n - Pose

Accession number: 20254919667846

Title of translation: 基于 BMR - YOLO 11n - Pose 的藠头关键点识别与分类方法

Authors: Liu, Haopeng (1, 2); Yang, Yunxiao (1); Kang, Qixin (1); Zhang, Guozhong (1, 2); Zhang, Leyan (1); Wei, Jia (1, 2)

Author affiliation: (1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China

Corresponding author: Wei, Jia(weijia@mail.hzau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 490-498

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the irregular morphology of Allium chinense, which led to the problems such as distorted feature representation and difficulty in extracting external contours during detection, a multiscale keypoint recognition and classification method based on deep learning was proposed. By identifying the apex and the two endpoints of the transverse diameter of Allium chinense, its external contour features were extracted. Simultaneously, a decision tree algorithm was employed to classify the size and shape characteristics of Allium chinense. Initially, using YOLO 11n - Pose as the baseline model, a BMR - YOLO 11n - Pose model was constructed by introducing a bi-level routing attention (BRA) mechanism in the neck, a feature enhancement layer (MobileNet Variants), and a reparameterized convolution with channel shuffle (RCS) . Compared with the baseline model, improvements of 0. 9 and 1. 9 percentage points in key point recognition accuracy (Pose - P) and bounding box recognition accuracy (Box - P) in the proposed model were achieved respectively, with an average precision (mAP0. 5 - 0. 95 ) increase of 1. 9 percentage points. Furthermore, based on the contour features, a decision tree algorithm was applied to classify the size of Allium chinense, the classification model accuracies were 92. 87% and 84. 72% respectively, representing improvements of 11. 19 and 8. 39 percentage points over the original baseline model, effectively enhancing the classification accuracy of Allium chinense appearance characteristics. The research result can provide theoretical references and technical support for application scenarios such as Allium chinense pose recognition and classification. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Deep learning

Controlled terms: Classification (of information)? - ?Contour followers? - ?Decision trees? - ?Morphology

Uncontrolled terms: Allium chinense identification? - ?Baseline models? - ?BMR YOLO 11n pose? - ?Classification methods? - ?Deep learning? - ?Keypoints? - ?Percentage points? - ?Point detection? - ?Recognition methods? - ?Target key point detection

Classification code: 214 Materials Science? - ?601.3 Mechanisms? - ?603 Machine Tools? - ?716.1 Information Theory and Signal Processing? - ?903.1 Information Sources and Analysis? - ?961 Systems Science? - ?1101.2.1 Deep Learning? - ?1201.5 Computational Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory

Numerical data indexing: Percentage 7.20E+01%, Percentage 8.70E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.047

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

21. Classification of Maize Leaf Disease Level Information from UAV Remote Sensing Images with Soil Background Removal

Accession number: 20254919668200

Title of translation: 基于无人机遥感影像土壤背景剔除的玉米叶部病害等级识别模型

Authors: Liu, Tao (1); Han, Yiman (1); Yang, Fengyuan (1); Liu, Wang (1); Zhang, Hui (2); Zhang, Huan (3)

Author affiliation: (1) College of Urban and Rural Planning, Henan University of Economics and Law, Zhengzhou; 450046, China; (2) College of Information and Management Sciences, Henan Agricultural University, Zhengzhou; 450046, China; (3) College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou; 450002, China

Corresponding author: Zhang, Huan(zhanghuan5754@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 408-417

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate acquisition of maize leaf disease level information is a key technical link to realize intelligent plant protection in the field. The issue of low accuracy in crop disease severity classification caused by soil background interference in UAV remote sensing images was addressed by proposing a maize leaf disease severity classification method that integrated vegetation index threshold segmentation with deep learning. Targeting multi-variety maize cultivation areas as the research object, high-resolution UAV image data were acquired and processed through image stitching, radiometric correction, and region of interest (ROI) cropping to construct sample datasets. Through data augmentation strategies, including rotation, flipping, and contrast enhancement, a balanced dataset containing 5 088 annotated images was developed. Soil background removal was achieved by using a combination of Otsu algorithm and vegetation indices. Multiple maize leaf disease severity recognition models were established by using both machine learning and deep learning approaches before and after soil background removal. Results showed that the green band (G-band) achieved optimal performance in soil background removal with a maximum intersection over union (IoU) value of 75. 9%; among machine learning models, the support vector machine (SVM) method based on EXG features achieved the highest classification accuracy, with accuracy (ACC), recall (R), and F1-score all exceeded 88. 0%; deep learning models outperformed machine learning models, with the ResNet50 network model achieved classification accuracy and F1-score of 94% and 94. 7%, respectively; soil background removal improved classification performance of the two types of models by 6. 16 percentage points and 5. 64 percentage points, respectively. The research result can provide a methodological reference for UAV low-altitude remote sensing monitoring of maize leaf diseases and offered technical support for precision farmland management in smart villages. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Deep learning

Controlled terms: Agricultural machinery? - ?Classification (of information)? - ?Cultivation? - ?Diseases? - ?Farms? - ?Grain (agricultural product)? - ?Image classification? - ?Image enhancement? - ?Learning systems? - ?Remote sensing ? - ?Rural areas? - ?Soils? - ?Support vector machines? - ?Unmanned aerial vehicles (UAV)? - ?Vegetation

Uncontrolled terms: Background removal? - ?Deep learning? - ?Disease grading model? - ?Disease severity? - ?Grading model? - ?Leaf disease? - ?Maize leaf disease? - ?Smart village? - ?Soil background? - ?UAV remote sensing

Classification code: 102.1.2 Health Science? - ?103 Biology? - ?403.2 Regional Planning and Development? - ?483.1 Soils and Soil Mechanics? - ?652.1 Aircraft? - ?716.1 Information Theory and Signal Processing? - ?731.1 Control Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?903.1 Information Sources and Analysis? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1106.3.1 Image Processing

Numerical data indexing: Percentage 0.00E00%, Percentage 7.00E+00%, Percentage 9.00E+00%, Percentage 9.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.039

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

22. Design and Experiment of Squirrel Cage Pressure Roller Type Suppression Device for Potato Full-scale Cultivator

Accession number: 20254919668119

Title of translation: 马铃薯全面耕耘机鼠笼压辊式镇压装置设计与试验

Authors: Lü, Yining (1); Zhao, Zhiming (2); Zhu, Xiaoxin (2); Liu, Jinni (2)

Author affiliation: (1) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou; 310058, China; (2) College of Engineering, Northeast Agricultural University, Harbin; 150030, China

Corresponding author: Zhu, Xiaoxin(neau_zxx@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 194-203

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of excessive soil disturbance, thickened plow pan, and intensified wind/ water erosion associated with conventional land preparation methods. It focused on the optimized design and performance analysis of a compaction device for a wide-width comprehensive tillage machine used in potato cultivation, systematically investigating soil fragmentation and leveling techniques during pre-planting land preparation. Combining theoretical analysis with numerical simulation, a contact mechanics model between the roller bars and soil was established to analyze the influence of device weight and soil interaction mechanisms on operational effectiveness. This approach identified optimal ranges for key structural and operational parameters of the compaction roller. An EDEM-based discrete element model of the soil-compaction device interaction was developed. A three-factor, five-level quadratic orthogonal rotation regression test was conducted, considering operational speed, roller bar diameter, and bar rotation angle as test factors, with soil fragmentation rate and surface leveling as evaluation metrics. Simulation results demonstrated soil fragmentation rate of 86. 32% and leveling precision of 21. 55 mm. Field validation tests confirmed significant performance improvements: the optimized device achieved soil fragmentation rate of 85. 31% (2. 09 percentage points higher than that of conventional equipment) and surface leveling precision of 25. 2 mm (6. 9 mm improvement over that of the ZY 180 roller). Experimental data verified that the designed cage-type roller not only integrated well with the host machine but also met agronomic requirements for potato field preparation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Compaction

Controlled terms: Agricultural implements? - ?Agricultural machinery? - ?Cultivation? - ?Discrete element methods? - ?Leveling (machinery)? - ?Rollers (machine components)? - ?Soil structure interactions? - ?Soil testing? - ?Soils? - ?Surface testing

Uncontrolled terms: Conservation tillage? - ?Discrete elements method? - ?Full-scale cultivator? - ?Levelings? - ?Potato? - ?Roller type? - ?Soil disturbances? - ?Squirrel-cage? - ?Suppression devices? - ?Suppressor devices

Classification code: 208 Coatings, Surfaces, Finishes, Films and Deposition? - ?215 Materials Testing? - ?483.1 Soils and Soil Mechanics? - ?483.2 Foundations? - ?601.2 Machine Components? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?913.4 Manufacturing? - ?1201.5 Computational Mathematics? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Percentage 3.10E+01%, Percentage 3.20E+01%, Size 2.00E-03m, Size 5.50E-02m, Size 9.00E-03m

DOI: 10.6041/j.issn.1000-1298.2025.11.018

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

23. Water Supply and Demand Effect and Control Zoning of Agricultural Production Space in Luohe River Basin of Eastern Qinling Mountains

Accession number: 20254919667838

Title of translation: 秦岭东段洛河流域农业生产空间水分供需效应及其管控分区

Authors: Ma, Quanlai (1, 2); Yang, Chongke (1, 2); Zhou, Hao (3); Yang, Yanwei (1, 2); Lu, Zhong (4); Tang, Zhengqing (5); Lü, Minmin (1, 2)

Author affiliation: (1) The First Institute of Resources and Environment Investigation of Henan Province, Zhengzhou; 450007, China; (2) Henan Provincial Research Center for Resources and Environment Investigation, Zhengzhou; 450007, China; (3) School of Geographical Sciences, Hunan Normal University, Changsha; 410081, China; (4) Yellow River Engineering Consulting Co., Ltd., Zhengzhou; 450003, China; (5) Henan Yudi Science and Technology Group Co.,Ltd., Zhengzhou; 450016, China

Corresponding author: Yang, Chongke(15290851067@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 667-676

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Water supply-demand imbalance was identified as a critical factor hindering agricultural sustainability in ecologically sensitive regions. Clarifying the water supply-demand dynamics in agricultural production spaces and implementing scientific irrigation management were crucial for ensuring food security and optimizing territorial spatial planning. Focused on the Luohe River Basin in the eastern Qinling mountains, the land use data (2000—2020), meteorological data, and geospatial datasets were utilized. A water balance assessment model was constructed by integrating the Penman Monteith equation, FU’ s formula, and an effective precipitation calculation model to precisely analyze water supply-demand equilibrium at a microscale grid level. A multi-agent spatial optimization model was further developed to delineate irrigation management zones. Key findings included as follows: agricultural land remained the dominant land use type in the basin, though its area was decreased by 1. 90% during the study period, with its spatial center of gravity shifting first northeastward and then westward. Significant spatiotemporal heterogeneity was observed in actual evapotranspiration and effective precipitation. Higher values were detected in the southern basin and western Lingbao City, while weaker values dominated central-eastern areas. Pe exhibited a gradual decline from south to north. The water surplus-deficit in agricultural zones showed an overall decreasing trend, with surplus areas concentrated in the eastern basin and expanding deficit zones. Drought risk escalated from moderate to severe levels over time. The proposed irrigation zoning methodology effectively balanced water equilibrium characteristics and spatial aggregation benefits of farmland, resulting in five management zones, including priority control area, key focus area, orderly management area, moderate guidance area and adaptive production area. Customized irrigation strategies were recommended based on zonal characteristics. These findings provided scientific support for implementing a “water-determined land use” strategy in territorial resource management within the basin. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Multi agent systems

Controlled terms: Agribusiness? - ?Agricultural robots? - ?Economics? - ?Food supply? - ?Irrigation? - ?Land use? - ?Landsat? - ?Water management? - ?Water supply

Uncontrolled terms: Agricultural production space? - ?Agricultural productions? - ?Eastern Qinling Mountains? - ?Luohe river basin of eastern qinling mountain? - ?Multi-Agent Model? - ?Partition? - ?River basins? - ?Supply/demand balance? - ?Water supply-demand balance? - ?Water-supply demand

Classification code: 403 Urban and Regional Planning and Development? - ?444 Water Resources? - ?446.1 Water Supply Systems? - ?655.1 Satellites? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?822.3 Food Products? - ?971 Social Sciences? - ?1101 Artificial Intelligence

Numerical data indexing: Percentage 9.00E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.065

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

24. Screening of Phenolic Inhibitors against α-glucosidase from Fermented Pecan Kernels by Lactic Acid Bacteria

Accession number: 20254919633971

Title of translation: 碧根果乳酸菌发酵液中酚类 α-葡萄糖苷酶抑制剂筛选

Authors: Pan, Lihua (1, 2); Chen, Luping (1); Yuan, Xue (1); Luo, Shuizhong (1, 2); Zheng, Zhi (1, 2)

Author affiliation: (1) School of Food and Biological Engineering, Hefei University of Technology, Hefei; 230009, China; (2) Anhui Province Key Laboratory of Agricultural Products Modern Processing, Hefei; 230009, China

Corresponding author: Luo, Shuizhong(luoshuizhong@hfut.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 716-725

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Effects of the biogenic transformation and α-glucosidase inhibitory activity of phenolics in pecan kernels by lactic acid bacteria fermentation were explored by combining the methods of ultrafiltration affinity-LC MS, component difference analysis of fermentation broth, molecular docking and enzyme inhibition kinetics. The results showed that the content of free phenols in the fermented pecan broth was increased by 18. 48%, and the IC50 of α-glucosidase was decreased by 57. 39% after 1% pecan nut pulp was fermented by Lactobacillus plantarum for 36 h. The enzyme activities of xylanase, cellulase and β-glucosidase in the fermented broth were positively correlated with the contents of free phenols. The results of component difference analysis showed that the contents of nine free phenols were up-regulated and those of five free phenols were down-regulated. The increased relative contents of catechin, epicatechin, methyl ellagic acid and epicatechin gallate after fermentation may be the main reason for the enhancement of α-glucosidase inhibitory activity of the free phenols in pecan broth. Eight potential α-glucosidase inhibitors were screened out from the 35 free phenols in the fermented pecan broth by ultrafiltration affinity screening and liquid chromatography-mass spectrometry. The results of molecular docking analysis showed that the binding energy of free phenolic compounds with α-glucosidase was equal to or even lower than that of acarbose, and their binding sites with α-glucosidase were mainly Asp609, Asp215, Glu277, Glu411 and His280. The screened free phenolics were competitive, non-competitive or mixed α-glucosidase inhibitors. The research result can provide a theoretical basis for the high-value processing and utilization of pecans, the design of phenolic α-glucosidase inhibitors and the research and development of phenolic hypoglycemic health products. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Ultrafiltration

Controlled terms: Binding energy? - ?Binding sites? - ?Docking? - ?Drug products? - ?Enzyme inhibition? - ?Fermentation? - ?Lactic acid? - ?Liquid chromatography? - ?Mass spectrometry? - ?Molecular docking ? - ?Molecular modeling? - ?Phenols? - ?Plants (botany)? - ?Screening

Uncontrolled terms: Glucosidase? - ?Glucosidase inhibitors? - ?Inhibitory activity? - ?Lactobacillus plantarum? - ?Molecular docking? - ?Pecan? - ?Phenolic inhibitors? - ?Phenolics? - ?Ultrafiltration affinity? - ?Α-glucosidase

Classification code: 101.7 Biotechnology? - ?102.2.1 Pharmaceutics and Drug Products? - ?103 Biology? - ?103.1 Microbiology? - ?801.1 Biochemistry? - ?801.3 Physical Chemistry? - ?802.2 Chemical Reactions? - ?802.3 Chemical Operations? - ?804.1 Organic Compounds? - ?805 Chemical Engineering? - ?1106.1 Computer Programming? - ?1301.1.3 Atomic and Molecular Physics? - ?1301.1.3.1 Spectroscopy

Numerical data indexing: Percentage 1.00E00%, Percentage 3.90E+01%, Percentage 4.80E+01%, Time 1.296E+05s

DOI: 10.6041/j.issn.1000-1298.2025.11.070

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

25. Method for Identifying Infection Structures of Cucumber Downy Mildew Based on Improved YOLO v7 Model

Accession number: 20254919668198

Title of translation: 基于改进 YOLO v7 的黄瓜霜霉病菌侵染结构识别方法

Authors: Qiao, Chen (1); Han, Zonghuan (1); Zhang, Yiding (2); Han, Mengyao (1); Zhang, Lingxian (1)

Author affiliation: (1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) College of Engineering, China Agricultural University, Beijing; 100083, China

Corresponding author: Zhang, Lingxian(zhanglx@cau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 528-537

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Cucumber downy mildew is caused by Pseudoperonospora cubensis through infection, severely impacting the quality and yield of cucumbers. Therefore, early diagnosis of infection structures is crucial for disease warning and prevention. A detection model for cucumber downy mildew infection structures (sporangia, spores, hyphae, and sporogenous structures) was proposed based on an improved YOLO v7 model. By introducing the squeeze-and-excitation attention mechanism (SE), the model enhanced its ability to extract features from small objects such as spores and sporangia. Additionally, the model used the normalized Wasserstein distance (NWD) to improve bounding box regression accuracy, enabling more precise localization of small targets like spores. This approach also alleviated the sensitivity of traditional IoU methods to positional deviations in small targets, thereby reducing errors and enhancing the robustness of the model. The dynamic head (DyHead) module was incorporated into the detection head, dynamically adjusting the receptive field size to improve multi-scale feature fusion. This was particularly effective in handling small objects and complex backgrounds, significantly enhancing multi-scale object detection performance. The study also explored the impact of different DyHead stacking numbers on model performance and computational complexity. Experimental results showed that when DyHead was stacked four times, the model achieved optimal mAP@ 0. 5 and mAP@ 0. 5:0. 95 values. The improved YOLO v7 model achieved an mAP@ 0. 5 of 86. 5%, a 3. 9 percentage points improvement over the original YOLO v7. In comparison with other advanced object detection models such as YOLO v3, YOLO v5s, YOLO v8x, SSD, and Faster R – CNN, the improved YOLO v7 demonstrated superior performance in both mAP@ 0. 5 and mAP@ 0. 5:0. 95, especially in detecting small objects and handling complex backgrounds with higher accuracy and robustness. Furthermore, based on the improved YOLO v7 model, a practical cucumber downy mildew infection structure recognition system was developed. This system featured an intuitive user interface and enabled accurate identification and quantitative analysis of infection structures, including spores, sporangia, hyphae, and sporulating structures. It provided an efficient solution for the early warning and prevention of cucumber downy mildew in agricultural production. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Object detection

Controlled terms: 3D modeling? - ?Feature extraction? - ?Object recognition? - ?Plant diseases? - ?Robustness (control systems)? - ?Structural optimization

Uncontrolled terms: Cucumber downy mildews? - ?Detection models? - ?Dynamic head? - ?Infection structure? - ?Normalized wasserstein distance? - ?SE? - ?Small objects? - ?Small targets? - ?Wasserstein distance? - ?YOLO v7

Classification code: 103 Biology? - ?731.1 Control Systems? - ?1101.2 Machine Learning? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1201.7 Optimization Techniques? - ?1201.12 Modeling and Simulation

Numerical data indexing: Percentage 5.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.051

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

26. Design and Test of Flexible Nail Teeth Type Residual Film Picking and Strapping Machine

Accession number: 20254919629877

Title of translation: 柔性钉齿式残膜捡拾打包作业机设计与试验

Authors: Shi, Zenglu (1, 2); Yao, Jieting (1); Zhang, Xuejun (1, 2); Liu, Xiaopeng (1); Yang, Longfei (1); Zhang, Chaoshu (3); Liu, Lei (3)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi; 830052, China; (3) Aksu City Tiandi Agricultural Machinery Manufacturing Co., Ltd., Aksu; 843000, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 308-319

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to solve some problems of existing residual film operation machinery, such as unstable operation performance, high impurity content of recycling mixture and complex structure of packaging device, a method of picking up, transporting, separating residual film and removing impurities by flexible nail teeth was proposed. Based on the principle of rolling and rubbing the V type belt residual film into bundles, a flexible nail teeth type of residual film picking and strapping machine was designed. The kinematics and dynamics characteristics of essential components such as flexible nail teeth picking mechanism, stripping device, and strapping device were researched. The structure size, arrangement, and working parameters of essential components were analyzed. The rotation speed of the film lifting device was 218. 2 r / min. The critical range of tooth tip linear velocity of continuous film picking was 1. 93 ~ 2. 33 m / s. The force of the stripping process was analyzed, and the effective stripping speed was 96 ~ 116 r / min. The force and motion parameters of strapping were analyzed and calculated, and the suitable strapping linear velocity was 1. 38 ~ 2. 00 m / s. Using the coupling method of ANSYS and smoothed particle hydrodynamics (SPH) to construct the simulation calculation model of the tooth-picking film, the stress change and average peak stress of the residual film was obtained, and compared with the tensile strength of the surface residual film covering 180 days in the current season to verify the design rationality of the picking mechanism. The prototype verification test showed that when the forward speed of the machine was 6 km / h, the linear speed of the pick-up tooth was 2. 33 m / s and the linear speed of the strapping belt was 1. 62 m / s. The average residual film pick-up rate of 5 hectares of continuous operation was 89. 7%, the impurity rate of the recycled mixture was 28. 6%, the film package forming rate was 100%, and the film package density was 90. 2 kg / m3 . The test results accorded with the performance requirements of national and industry standards and provided technical references for the research and development of residual film recycling packers. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Strapping

Controlled terms: Agricultural machinery? - ?Belts? - ?Nails? - ?Pickups? - ?Recycling? - ?Removal

Uncontrolled terms: Flexible nail tooth type? - ?Linear speed? - ?Linear velocity? - ?Picking machines? - ?Residual film operation machine? - ?Residual film picking and strapping? - ?Residual films? - ?Strapping machines? - ?Tooth type

Classification code: 214 Materials Science? - ?601.2 Machine Components? - ?602.2 Mechanical Transmissions? - ?605.2 Small Tools, Unpowered? - ?694.1 Packaging Materials and Equipment? - ?752 Sound Devices, Equipment and Systems? - ?802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?1501.3.1 Recycling Waste

Numerical data indexing: Age 4.932E-01yr, Angular velocity 1.6032E+00rad/s to 1.9372E+00rad/s, Angular velocity 3.34E-02rad/s, Area 5.00E+04m2, Linear density 2.00E+00kg/m, Percentage 1.00E+02%, Percentage 6.00E+00%, Percentage 7.00E+00%, Size 6.00E+03m, Velocity 0.00E00m/s, Velocity 3.30E+01m/s, Velocity 6.20E+01m/s

DOI: 10.6041/j.issn.1000-1298.2025.11.029

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

27. Design and Experiment of Tractor Front Suspension Finger Type Rape Windrower

Accession number: 20254919628588

Title of translation: 拖拉机前悬挂拨指式油菜割晒机设计与试验

Authors: Shu, Caixia (1, 2); Qin, Yiming (1); Liao, Qingxi (1, 2); Zhang, Qingsong (1, 2); Yuan, Jiacheng (1); Wan, Xingyu (1, 2)

Author affiliation: (1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China

Corresponding author: Wan, Xingyu(wanxy@mail.hzau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 285-297

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the difficulty of the lateral conveying of rapeseed plants, which was easy to be blocked and the quality of lateral laying needed to be improved in the operation process of the side-laid windrower, combined with the reality of the large number of tractors, a tractor front-suspension finger type windrower was developed, and the chain toggle finger conveying device and the stepped discharge port were used to restrain the stable transportation and orderly discharge of rapeseed stalks. Dynamics and kinematics were used to analyze the motion process of support feeding, transverse transport and differential crop removal after cutting of rape stem. The optimal parameter range of each key component was determined based on the material characteristics of rape stem and lateral laying mechanism. The variation coefficient of lay-up angle, the difference of lay-up angle between upper and lower layers, the variation coefficient of lay-up relaxation and the variation coefficient of lay-up height were further used as evaluation indexes. The quadratic regression orthogonal combination test of lay-up mass was carried out to determine the optimal parameter combination among the operating parameters, and the field verification test was carried out. The results of dynamic and kinematic analysis showed that the optimal parameter range of rape windrower of the rotating speed of the wheeling wheel was 46 ~ 72 r / min, the angle of the transverse conveying device of the cutting table was 35°, the installation distance of the chain dipper finger was 240 mm, the linear speed of the transverse conveying device was 1. 1 ~ 1. 7 m / s. Quadratic regression orthogonal combination test results showed that the linear speed of the transverse conveying device had the most significant influence on the lay-up angle, lay-up angle difference, lay-up relaxation variation coefficient and lay-up height variation coefficient. Under the optimal combination of parameters, the speed of the wheel was 72 r / min, the linear speed of the transverse conveying speed was 1. 6 m / s, and the forward speed of the machine was 1. 6 m / s. Field experiments showed that during the operation of the windrower, the rapeseed plants were transported smoothly and laid in an orderly manner, and the average laying angle of stalks was 79. 9° under optimal parameter combination conditions. The average lay-up angle difference between upper and lower layers was 10. 7°, the coefficient of variation of paving width was 9. 2%, the coefficient of variation of paving height was 9. 1%, and the operation efficiency of machinery and tools was 0. 75 hm2 / h. The research can provide reference for improving and optimizing the structure of rapeseed windrower. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Kinematics

Controlled terms: Chains? - ?Conveying? - ?Crops? - ?Oilseeds? - ?Plants (botany)? - ?Regression analysis? - ?Rotating machinery? - ?Suspensions (components)? - ?Tractors (agricultural)? - ?Tractors (truck) ? - ?Wheels

Uncontrolled terms: Chain finger? - ?Linear speed? - ?Optimal parameter? - ?Parameter optimization? - ?Parameter range? - ?Rape windrower? - ?Rapeseed? - ?Tractor front suspension? - ?Variation coefficient? - ?Windrowers

Classification code: 103 Biology? - ?601.1 Mechanical Devices? - ?601.2 Machine Components? - ?601.3 Mechanisms? - ?602.1 Mechanical Drives? - ?663.1 Heavy Duty Motor Vehicles? - ?692.1 Conveyors? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?1202.2 Mathematical Statistics? - ?1301.1.1 Mechanics

Numerical data indexing: Angular velocity 1.2024E+00rad/s, Angular velocity 7.682E-01rad/s to 1.2024E+00rad/s, Percentage 1.00E00%, Percentage 2.00E+00%, Size 2.40E-01m, Velocity 6.00E+00m/s, Velocity 7.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2025.11.027

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

28. Individual Identification Method for Dairy Cows Based on FF GEI

Accession number: 20255019669490

Title of translation: 基于逐帧步态能量图的奶牛个体识别方法

Authors: Si, Yongsheng (1, 2); Yang, Jiao (1, 2); Wang, Bin (1, 2); Ma, Yabin (3); Yuan, Ming (3)

Author affiliation: (1) College of Information Science and Technology, Hebei Agricultural University, Baoding; 071001, China; (2) Key Laboratory of Agricultural Big Data of Hebei Province, Baoding; 071001, China; (3) Hebei Provincial Station for Livestock Varieties Producing and Spreading, Shijiazhuang; 050061, China

Corresponding author: Ma, Yabin(dhimyb@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 572-580

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Individual identification of dairy cows is the prerequisite and foundation for analyzing dairy cow behavior. To solve the problem of limited patterns or poor recognition performance of pure color dairy cows in machine vision based individual identification of dairy cows, a dairy cow individual identification method was proposed based on frame by frame gait energy image (FF GEI). Firstly, the dairy cow’s hooves were automatically recognized and the motion cycle was divided based on changes in the position of the hooves. On this basis, DeepLabv3 + was utilized to preprocess image sequences of a motion cycle and generate frame by frame gait energy image. Then, the AlexNet network and the LSTM long and short-term memory network were constructed as the AlexNet LSTM network. The AlexNet LSTM network was introduced with the SENet attention mechanism to generate the SE AlexNet LSTM model. Individual identification experiments were conducted on a total of 1 656 video datasets from 40 dairy cows. The results showed that there was little difference between the motion cycles automatically divided by the proposed method and those manually divided. For dairy cows with different health states of healthy, lame, and from healthy to lame, the SE AlexNet LSTM model achieved individual identification accuracies of 95. 59%, 93. 98%, and 87. 81%, respectively, with an average accuracy of 91. 46% and a fast convergence rate. The results can provide an effective technical support for machine vision-based individual dairy cow identification. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Long short-term memory

Controlled terms: Agricultural machinery? - ?Computer vision? - ?Dairies? - ?Machine Perception? - ?Machine vision? - ?Time and motion study

Uncontrolled terms: Cow behavior? - ?Cycle of motion? - ?Dairy cow? - ?Gait energy images? - ?Identification method? - ?Individual identification? - ?Individual recognition? - ?Machine-vision? - ?Motion cycle? - ?Vision based

Classification code: 101.5 Ergonomics and Human Factors Engineering? - ?821.2 Agricultural Machinery and Equipment? - ?822.1 Food Products Plants and Equipment? - ?912.1 Industrial Engineering? - ?1101.2.1 Deep Learning? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 4.60E+01%, Percentage 5.90E+01%, Percentage 8.10E+01%, Percentage 9.80E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.055

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

29. Study on Drying Characteristics of Alfalfa under Different Pretreatments and Drying Methods

Accession number: 20254919668190

Title of translation: 不同预处理与干燥方式下苜蓿干燥特性研究

Authors: Sun, Qingyun (1, 2); Han, Menglong (1); Yu, Xianlong (1, 2); Zhao, Feng (1, 2); Jia, Zhenchao (1, 2); Wu, Wenxuan (1, 2); Zhang, Zongchao (1, 3)

Author affiliation: (1) Shandong Academy of Agricultural Machinery Science, Ji’nan; 250100, China; (2) Huang – Huai – Hai Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Ji’nan; 250100, China; (3) Jinan Key Laboratory of Intelligent Equipment for Post-production Loss Reduction of Agricultural Products, Ji’nan; 250100, China

Corresponding author: Zhang, Zongchao(zhangzchao@foxmail.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 177-183

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Due to the high initial moisture content of alfalfa and the unreasonable drying process, the problems of high drying energy consumption and poor quality of alfalfa was caused. The effects of different pretreatment methods (fracturing, sun dried, and crushing dehydration) and drying methods (infrared drying 60℃, hot air drying 60℃) on the drying characteristics and quality of alfalfa were investigated. The drying model for alfalfa was established, and the optimal energy-saving drying process was determined. The experimental results showed that the infrared drying time under different pretreatment conditions was shortened by 21. 6% ~ 45. 3% compared with that by the hot air drying method. Under the same drying method, the drying time of the sun dried pretreatment group was the shortest due to the low initial moisture content of alfalfa, which was reduced by more than 70% compared with the untreated group. The drying time of the crushing dehydration pre-treatment group was the second shortest. Under different drying methods, the red-green value of alfalfa was lower,and the yellow and blue value of alfalfa was higher in the untreated and fracturing pretreatment groups, which indicating the least color loss. The color loss of sun dried pretreatment group was relatively larger than that of others pretreatments, where the brightness value and the yellow and blue value were increased due to the loss of chlorophyll caused by long-term drying. The crude protein content of alfalfa after sun dried pretreatment was the highest. The relative feed value (RFV) of alfalfa in the sun dried pretreatment group was the highest, with a RFV value greater than 150, which was 11. 37 ~12. 95 higher than that of the untreated group. The crude protein content and RFV value of the crushing dehydration pretreatment group were the lowest, which was due to that the alfalfa juice squeezed out and the structure of the leaves and stems significantly damaged. The optimal drying process for alfalfa was the sun dried pretreatment and 60℃ infrared drying group. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Infrared drying

Controlled terms: Color? - ?Crushing? - ?Curing? - ?Dehydration? - ?Energy conservation? - ?Energy utilization? - ?Moisture? - ?Moisture determination? - ?Solar dryers

Uncontrolled terms: Alfalpha? - ?Drying characteristics? - ?Drying methods? - ?Drying process? - ?Drying time? - ?Hot air drying? - ?Initial Moisture Content? - ?Pre-treatments? - ?Pretreatment methods? - ?Relative feed

Classification code: 214 Materials Science? - ?741.1 Light/Optics? - ?802.2 Chemical Reactions? - ?822.2 Food Processing Operations? - ?941.6 Moisture Measurements? - ?1008.4 Solar Energy Conversion and Power Generation? - ?1009.1 Energy Conservation? - ?1009.2 Energy Consumption

Numerical data indexing: Percentage 3.00E+00%, Percentage 6.00E+00%, Percentage 7.00E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.016

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

30. Tea Leaf Grading Recognition Method Based on TS YOLO

Accession number: 20254919668130

Title of translation: 基于 TS YOLO 的茶叶分级识别方法

Authors: Tan, Zhiying (1, 2); Zhang, Dong (1, 2); Wu, Yanhao (1, 2); Li, Xu (1, 2); Xu, Xiaobin (1, 2); Li, Tao (3)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Hohai University, Changzhou; 213200, China; (2) Jiangsu Key Laboratory of Special Robot Technology, Hohai University, Changzhou; 213200, China; (3) School of Mechanical Engineering and Rail Tramsit, Changzhou University, Changzhou; 213164, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 509-516

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the problem of insufficient recognition accuracy and low efficiency in grading of tea buds due to the large number and diverse postures of tea buds after mechanized harvesting in the industrial conveyor belt sorting scenario, a fast recognition model TS YOLO was proposed based on the improved YOLO v8n. Firstly, a lightweight network CSFCN was introduced to solve the problems of context mismatch and spatial feature alignment, thereby improving the detection accuracy. Secondly, a convolution module Tconv specifically optimized for the tea scene in the pipeline was designed to enhance the ability of rapid feature extraction. For the problem of target occlusion, the detection head was improved by adopting MultiSEAM. Finally, Wise CIoU was introduced as the bounding box loss function to more accurately measure the similarity between targets and accelerate model convergence and improve detection accuracy. Experimental results showed that the TS YOLO model achieved a precision rate of 94. 0%, a recall rate of 91. 6%, and an mAP of 94. 30%, which were 1. 0 percentage, 1. 8 percentage, and 1. 6 percentage points higher than those of the original YOLO v8n, respectively. Moreover, its detection speed reached 148. 98 f / s on the test set, which was 40% higher than YOLO v8n and 42. 5 f / s faster. The improved TS YOLO model met the technical requirements for online tender leaf sorting in large-scale tea production lines in terms of detection accuracy and recognition speed. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Grading

Controlled terms: Belt conveyors? - ?Screening? - ?Sorting? - ?Tea

Uncontrolled terms: CSFCN? - ?Detection accuracy? - ?Mechanized harvesting? - ?MultiSEAM detection head? - ?Recognition accuracy? - ?Recognition methods? - ?Tconv? - ?Tea grading recognition? - ?Tea-leaves? - ?YOLO v8n

Classification code: 692.1 Conveyors? - ?802.3 Chemical Operations? - ?822.3 Food Products? - ?1106.2 Data Handling and Data Processing

Numerical data indexing: Percentage 0.00E00%, Percentage 3.00E+01%, Percentage 4.00E+01%, Percentage 6.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.049

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

31. Review and Prospect of Full-process Mechanization Technology in Forage Production and Processing

Accession number: 20254919668168

Title of translation: 饲草生产与加工机械化技术研究现状与展望

Authors: Wang, Decheng (1); Li, Sibiao (1); You, Yong (1); Hui, Yunting (1); Ma, Pengbo (1); Xi, Junhui (1); Yan, Cuiqiang (1); Zhou, Xiaoyi (1); Hu, Yunsheng (1); Wang, Zhenhua (2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing; 100083, China

Corresponding author: Wang, Zhenhua(wzhh2008@yeah.net)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 1-20

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The forage industry serves as a key pivot point for resolving structural contradictions in agriculture and constructing an industrial pattern of “coordinated development of grain, cash crops and forage, and integration of agriculture and animal husbandry”. Enhancing mechanization level in forage production and processing constitutes a strategic initiative to ensure stable livestock product supply and strengthen food security barrier. Focusing on mechanization technologies for forage production and processing, starting from the connotation and technical route of full-process mechanization in forage production, it systematically sorted out the current status of technical research and equipment development in four major areas—forage seed production, grassland/ meadow forage rejuvenation and promoting-growth, forage cutting and harvesting, and forage processing and utilization. It paid special attention to the current situation and future development trends of relevant technologies and equipment in terms of national strategic needs, matching agronomic requirements, key technological innovations, breakthroughs in core components, application of intelligent equipment and so on. Compared with the advanced international standards characterized by “intelligent integration—ecological coordination—full-chain adaptation”, there were still core challenges on mechanization technologies for forage production and processing in China, including unbalanced and inadequate development of full-process mechanization equipment, high dependence on imports for core technologies and components, insufficient adaptability of scene equipment lacking in the depth of multidisciplinary integration and so on. In the future, it is imperative to accelerate efforts to address gaps in forage seed harvesters, forage equipment for hilly areas, and movable processing units, while overcoming bottlenecks in critical components such as knotters, precision seed metering devices, and cutting blades. Scene-specific intelligence should be promoted for natural grasslands and large-scale farmlands among other contexts. Through multidisciplinary integration of agronomy, ecology, and digital technologies, advanced technologies were merged to achieve independent innovations in core technologies and improve intelligence of equipment, the adaptability and efficiency of intelligent grass equipment can be enhanced, thereby driving the sustainable development of smart forage grass industry, solid support can be provided for “comprehensive agricultural outlook and all-encompassing approach to food” and agricultural modernization. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 121

Main heading: Seed

Controlled terms: Agronomy? - ?Crops? - ?Cutting? - ?Cutting tools? - ?Food supply? - ?Grain (agricultural product)? - ?Harvesters? - ?Integration? - ?Mechanization? - ?Modernization ? - ?Plants (botany)

Uncontrolled terms: Core components? - ?Core technology? - ?Cutting and flattening? - ?Forage? - ?Forage production? - ?Grassland restorations? - ?Industrial patterns? - ?Intelligent equipment? - ?Mechanisation? - ?Pivot point

Classification code: 103 Biology? - ?603.1 Machine Tool Accessories? - ?604.1 Metal Cutting? - ?607 Mechanical Engineering, Other Topics? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?822.3 Food Products? - ?901 Engineering Profession? - ?1201.2 Calculus and Analysis

DOI: 10.6041/j.issn.1000-1298.2025.11.001

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

32. Binocular Vision-based Spatial Positioning Method for Cow Automatic Teat-cup-attachment Utilizing Improved YOLO v8n

Accession number: 20255019668792

Title of translation: 基于改进 YOLO v8n 的双目视觉自动套杯奶牛乳头空间定位方法

Authors: Wang, Juan (1, 2); Li, Mengjie (1); Liu, Yaju (3); Fu, Xinpei (1); Li, Sirui (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding; 071001, China; (2) Hebei Key Laboratory of Intelligent Equipment and New Energy Utilization of Livestock and Poultry Breeding, Baoding; 071001, China; (3) Bohai College, Hebei Agricultural University, Cangzhou; 061100, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 560-571

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Manual teat-cup-attachment is labor-intensive and has low production efficiency, while automatic attachment involves higher costs, posing a certain economic burden for small and medium-sized dairy farms. To meet the needs of these farms and address challenges such as the complex milking environment and varying teat morphologies in rapid and accurate automatic teat-cup-attachment for milking machines, a novel binocular vision positioning method was proposed based on an improved YOLO v8n. This method enabled accurate detection and rapid localization of cow teats. The CSPDarknet backbone network of YOLO v8n was replaced with the lightweight FasterNet backbone network, a P2 feature layer was constructed in the neck network, and an EMA attention mechanism was added to the Detect and C2f sections, enhancing the model’s detection accuracy and speed for cow teats. The RANSAC algorithm was employed to optimize SURF feature points, reducing mismatches caused by the similarity between teats and udders. Based on the improved YOLO v8n model, binocular vision was used to acquire three-dimensional spatial information of cow teats. Ablation and comparative experiments conducted on a self-built dataset showed that the improved YOLO v8n model achieved a mAP@ 0. 5 of 98. 62%, a precision of 97. 23%, and a recall of 96. 69%, representing improvements of 2. 17, 3. 28, and 3. 65 percentage points, respectively, compared with that of the original YOLO v8n model. The parameter count was reduced to approximately half, and the frame rate was increased by a factor of 2. 31, significantly enhancing the model’s detection performance. Statistical analysis revealed that the average absolute deviation of teat positioning was 0. 011 7 m, with a variance of 0. 000 1 m2 and a standard deviation of 0. 011 9 m, meeting the requirements for the next stage of automatic teat cup attachment in milking machines. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Binocular vision

Controlled terms: Binoculars? - ?Computer vision? - ?Feature extraction? - ?Milking machines? - ?Network layers? - ?Statistics? - ?Tea

Uncontrolled terms: Automatic teat-cup-attachment? - ?Back-bone network? - ?Cow? - ?Improved YOLO v8n? - ?Positioning methods? - ?Spatial location? - ?Spatial positioning? - ?Teat detection? - ?Teat spatial location? - ?Vision based

Classification code: 741.2 Vision? - ?741.3 Optical Devices and Systems? - ?821.2 Agricultural Machinery and Equipment? - ?822.3 Food Products? - ?1101.2 Machine Learning? - ?1105 Computer Networks? - ?1106.8 Computer Vision? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 2.30E+01%, Percentage 6.20E+01%, Percentage 6.90E+01%, Size 1.00E00m, Size 7.00E+00m, Size 9.00E+00m

DOI: 10.6041/j.issn.1000-1298.2025.11.054

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

33. Yield Estimation of Winter Wheat Based on Multiple Remotely Sensed Parameters and Stacking Ensemble Learning

Accession number: 20254919634151

Title of translation: 基于遥感多参数和 Stacking 集成学习的冬小麦单产估测

Authors: Wang, Pengxin (1, 2); Wang, Jingyi (1, 2); Guo, Fengwei (1, 2); Liu, Junming (3); Li, Hongmei (4); Ye, Xin (1)

Author affiliation: (1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing; 100083, China; (3) College of Land Science and Technology, China Agricultural University, Beijing; 100193, China; (4) Shaanxi Provincial Meteorological Bureau, Xi’an; 710014, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 369-377

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to explore the application potential of model fusion in crop yield estimation and further improve the accuracy of winter wheat model for yield estimation, Guanzhong Plain in Shaanxi Province was taken as the study area, vegetation temperature condition index (VTCI),leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR), which were closely related to the growth and development of winter wheat, were selected as remotely sensed parameters, and a Stacking ensemble learning yield estimation model was constructed based on multi-model fusion. Considering the differences in training process among various machine learning algorithms, the long short-term memory (LSTM) network, support vector machine (SVM), elastic net regression (ENet), eXtreme gradient boosting (XGBoost), gradient boosting decision trees (GBDT) and random forest (RF) as alternative base models. Pearson correlation coefficient was used to quantify the correlation between estimation errors of the alternative base models, and according to the average correlation coefficient of prediction errors for each alternative base model, LSTM, SVM, ENet, and XGBoost were selected as the base models with the lowest average correlation coefficients of prediction errors. The estimated yields from each of the base models were used as meta-features and a linear regression model was employed as the meta-model to fit these meta-features and construct a Stacking ensemble model for winter wheat yield estimation. The results showed that compared with single yield estimation model with the highest accuracy, SVM, the Stacking ensemble model exhibited improved estimation accuracy (R2 = 0. 67, RMSE = 520. 50 kg / hm2 , MAPE = 9. 21%), indicating an increase in R2 by 0. 03, and reductions in RMSE and MAPE by 26. 28 kg / hm2 and 0. 83 percentage points, respectively. Therefore, the Stacking ensemble yield estimation model, by integrating the strengths of individual models, achieved more precise yield estimation results. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Long short-term memory

Controlled terms: Correlation methods? - ?Crops? - ?Decision trees? - ?Learning systems? - ?Linear regression? - ?Random errors? - ?Random forests? - ?Support vector regression? - ?Vegetation

Uncontrolled terms: Base models? - ?Ensemble learning? - ?Estimation models? - ?Leaf Area Index? - ?Stacking ensemble learning? - ?Stackings? - ?Support vectors machine? - ?Vegetation temperature condition index? - ?Winter wheat? - ?Yield estimation

Classification code: 103 Biology? - ?731.1.1 Error Handling? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.5 Agricultural Products? - ?961 Systems Science? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1201.5 Computational Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1202.2 Mathematical Statistics

Numerical data indexing: Mass 2.80E+01kg, Mass 5.00E+01kg, Percentage 2.10E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.035

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

34. Construction and Evaluation of Prediction Model for Disease and Growth of Farmed Fish Based on MTL–LSTM–SAT

Accession number: 20255019668598

Title of translation: 基于 MTL – LSTM – SAT 的养殖鱼类患病及生长预测模型构建与试验

Authors: Wang, Zhen (1, 2); Chen, Cong (1); Cao, Guangqiao (1, 3); Zhu, Hong (4); Shen, Qiyang (4); Zhao, Wei (2)

Author affiliation: (1) Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing; 210014, China; (2) Xianning Academy of Agricultural Sciences, Xianning; 437000, China; (3) Graduate School, Chinese Academy of Agricultural Sciences, Beijing; 100081, China; (4) Jiangsu Provincial Agricultural Machinery Development and Application Center, Nanjing; 210014, China

Corresponding author: Cao, Guangqiao(caoguangqiao@126.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 590-598 and 620

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: With the development of intensive aquaculture, the demand for precise water quality monitoring and fish health assessment has increased significantly. A multi-task learning model was proposed based on long short-term memory networks with a soft attention mechanism (MTL – LSTM – SAT) to jointly predict fish growth rate and disease incidence by using multimodal water quality data. Nile tilapia (Oreochromis niloticus) cultured in an aquaculture base in Xianning, Hubei Province, was selected as the experimental subject. Time-series data of environmental factors such as pH value, water temperature, dissolved oxygen, ammonia nitrogen concentration, and feeding amount, as well as fish body weight and health condition, were collected to construct the model input. The LSTM structure captured temporal patterns in water quality dynamics, while the integrated soft attention mechanism enabled the model to focus on critical time steps, enhancing its modeling capability for complex environmental fluctuations. Experimental results showed that the proposed model achieved a mean absolute error (MAE) of 0. 074 g/ d and root mean square error (RMSE) of 0. 092 g/ d in growth rate prediction, and a MAE of 0. 000 97 and RMSE of 0. 001 22 in disease incidence prediction. These results outperformed baseline models, including RNN, GRU, and single-task learning approaches. Multiple comparative and ablation experiments further demonstrated the model’s superior accuracy, robustness, and generalization performance. The proposed method offered a promising solution for intelligent fish farming and provided a valuable tool for aquaculture management and decision-making. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Main heading: Water quality

Controlled terms: Ammonia? - ?Aquaculture? - ?Decision making? - ?Fish? - ?Forecasting? - ?Learning systems? - ?Long short-term memory? - ?Mean square error? - ?Multi-task learning? - ?Prediction models

Uncontrolled terms: Attention mechanisms? - ?Disease incidence? - ?Farmed fishes? - ?Intensive aquacultures? - ?Mean absolute error? - ?Multitask learning? - ?Prediction modelling? - ?Root mean square errors? - ?Temporal memory? - ?Water quality monitoring

Classification code: 445.2 Water Analysis? - ?804.2 Inorganic Compounds? - ?821.4 Agricultural Methods? - ?822.3 Food Products? - ?912.2 Management? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1202 Statistical Methods? - ?1202.2 Mathematical Statistics

Numerical data indexing: Mass 7.40E-02kg, Mass 9.20E-02kg, Size 5.588E-01m

DOI: 10.6041/j.issn.1000-1298.2025.11.057

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

35. Spatial Prediction of Soil Heavy Metal Hg Content Based on Hyperspectral Data as Covariates

Accession number: 20254919634375

Title of translation: 基于高光谱协同的土壤重金属 Hg 含量空间预测

Authors: Yang, Qiyong (1); Li, Wenjun (1); Xiao, Qiong (2); Zhang, Cheng (2)

Author affiliation: (1) Agriculture Forestry Ecology College, Shaoyang University, Shaoyang; 422000, China; (2) Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin; 541004, China

Corresponding author: Yang, Qiyong(yangqiyong0739@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 651-657 and 676

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: When the heavy metals content in the soil accumulates to a certain level, it not only hinders the healthy growth of living organisms, but also poses a potential threat to the stability of the ecosystem. In order to achieve rapid measurement of soil heavy metal Hg content based on coKriging (COK) with soil hyperspectral data as covariates, totally 368 soil samples at 0 ~ 20 cm depth were collected as research objects from Shazi Town in northeast of Guangxi Zhuang Autonomous Region. The raw hyperspectral reflectance (R) of soil samples was measured by the standard procedure with an ASD FieldSpec4 instrument, which was transformed to eight spectral indices, such as the first order differential reflectance (FDR), the second order differential reflectance (SDR), reciprocal of reflectance (RR), differential for reciprocal of reflectance (RDR), logarithm of reflectance (LR), differential for logarithm of reflectance (LDR), square root of reflectance (SQR), differential for square root of reflectance (SQDR). The relationship between the soil heavy metal Hg content and the raw hyperspectral reflectance and its eight transformation was analyzed, and the spectral characteristic bands of the optimal transformation method were selected as auxiliary variables for the coKriging (COK) analysis of spatial prediction. The results showed that the content of heavy metal Hg in the soil of study area had significant correlations with the reflectance of the nine types of soil spectra. The LDR can better represent the relationship characteristics between soil spectra and the content of heavy metal Hg in the soil. Compared with the ordinary Kriging method (OK), the COK method can not only enhance the robustness of the model but also reduce the error of the model. As the number of collaborative variables increased, the upward trend of the robustness and prediction accuracy of the COK model was weakened. The three-coefficient variable COK model composed of 1 412 nm, 1 052 nm and 738 nm was the optimal COK analysis model for the study area. The R2 between the predicted data and the validation data was increased from 0. 117 of the OK to 0. 592 of COK, while the RMSE and MAE were decreased from 0. 214 mg / kg and 0. 247 mg / kg to 0. 167 mg / kg and 0. 154 mg / kg, respectively. This indicated that the COK model constructed based on easily obtainable soil spectral information can provide an economical and effective method for spatial prediction of soil heavy metal content. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Forecasting

Controlled terms: Kriging? - ?Metal analysis? - ?Reflection? - ?Soil surveys? - ?Soils

Uncontrolled terms: Auxiliary variables? - ?Co-Kriging? - ?Content-based? - ?Covariates? - ?Heavy metals content? - ?HyperSpectral? - ?Hyperspectral Data? - ?Soil heavy metals? - ?Soil sample? - ?Spatial prediction

Classification code: 201.1 Metallurgy and Metallography? - ?405.3 Surveying? - ?483.1 Soils and Soil Mechanics? - ?1201.9 Numerical Methods? - ?1202 Statistical Methods? - ?1301.1.3.1 Spectroscopy? - ?1301.3 Optics

Numerical data indexing: Mass 1.54E-04kg, Mass 1.67E-04kg, Mass 2.14E-04kg, Mass 2.47E-04kg, Size 0.00E00m to 2.00E-01m, Size 4.12E-07m, Size 5.20E-08m, Size 7.38E-07m

DOI: 10.6041/j.issn.1000-1298.2025.11.063

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

36. Design and Experiment of Missing Seeding Detection and Automatic Reseeding System of Potato Planter

Accession number: 20254919629865

Title of translation: 马铃薯播种机漏播检测与自动补种系统设计与试验

Authors: Yang, Ranbing (1, 2); Liu, Sha (1); Zhang, Huan (1); Pan, Zhiguo (1); Wu, Hongzhu (3); Li, Yang (4); Li, Xinlin (1); Shi, Yue (1); Deng, Zhixi (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao; 266109, China; (2) School of Mechanical and Electrical Engineering, Hainan University, Haikou; 570228, China; (3) Qingdao Hongzhu Agricultural Machinery Co., Ltd., Qingdao; 266300, China; (4) Menoble Co., Ltd., Beijing; 100083, China

Corresponding author: Pan, Zhiguo(peter_panzg@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 204-211

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the serious problem of seed omission in the high-speed operation of potato planters, a seed omission detection and automatic reseeding device was designed based on a spoon belt type seed metering device and with a PLC as the control core. The device used a group of photoelectric sensors to detect the seed omission of the spoon belt type seed metering device and the reseeding device respectively, achieving precise positioning of the seed spoon omission position. An automatic reseeding device with a reseeding device and a double electromagnetic iron push rod as the core was designed to achieve continuous and precise reseeding at the omission position. At the same time, the principle of accelerated reseeding was adopted to realize the self-reseeding of the reseeding device, avoiding the influence of unsuccessful reseeding on the reseeding effect of the spoon belt type seed metering device. Control programs were written according to the requirements of seed omission detection and reseeding control, and a human-machine interface was designed to achieve automatic reseeding. The detection effect of seed omission and the success rate of reseeding were tested on a test bench with the seed metering belt speed ranging from 0. 24 m / s to 0. 64 m / s. The results showed that the detection accuracy of seed omission was 100%, the original seed omission rate was from 6. 08% to 13. 64%, and the seed omission rate after reseeding was from 0. 72% to 1. 96%, with an average reseeding success rate of 87. 16% . The test results indicated that the seed omission detection and automatic reseeding device had good working performance and stable reseeding effect for continuous seed omission of the seed spoon, meeting the seeding operation requirements of potato planters under high-speed operation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Seed

Controlled terms: Man machine systems? - ?Photoelectric devices? - ?Plants (botany)

Uncontrolled terms: Automatic reseeding device? - ?Electromagnetic iron? - ?High-speed operation? - ?Missing seeding detection? - ?Photoelectric sensors? - ?Potato planter? - ?Precise positioning? - ?Push rods? - ?Seed-metering device? - ?Spoon-belt type seed-metering device

Classification code: 103 Biology? - ?741.3 Optical Devices and Systems? - ?821.5 Agricultural Products? - ?1107 Human-Machine Systems

Numerical data indexing: Percentage 1.00E+02%, Percentage 1.60E+01%, Percentage 6.40E+01%, Percentage 7.20E+01% to 1.00E00%, Percentage 8.00E+00% to 1.30E+01%, Percentage 9.60E+01%, Velocity 2.40E+01m/s to 0.00E00m/s, Velocity 6.40E+01m/s

DOI: 10.6041/j.issn.1000-1298.2025.11.019

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

37. Method for Determining Maturity of Alfalfa Seed Pods in Field Based on VSC – DETR

Accession number: 20255019668696

Title of translation: 基于 VSC – DETR 的田间环境苜蓿种荚成熟度判别方法

Authors: Yu, Zhenwei (1); Tian, Fuyang (1, 2); Wang, Qiang (3); Yan, Yinfa (1); Zhang, Yinuo (2); Song, Zhanhua (1)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China; (2) Shandong Higher Education Institution Future Industry Engineering Research Center of Intelligent Agricultural Robots, Taian; 271018, China; (3) Chinese Academy of Agricultural Mechanization Sciences Co., Ltd., Hohhot Branch, Hohhot; 010010, China

Corresponding author: Song, Zhanhua(songzh@sdau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 84-92

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Uneven maturity of alfalfa seeds hinders to determination of the optimal harvesting time. There are several algorithms used to assess the maturity level of alfalfa seed; however, existing algorithms are inefficient under natural conditions due to low automation and poor generalization capabilities. An efficient alfalfa seed pod maturity identification model (VSC – DETR) that can accurately determine the optimal harvest time was proposed. The proposed real-time detection Transformer (RT – DETR) model enhanced computational efficiency and generalization ability by replacing the main network of RT – DETR with a VanillaNet network, improving the model’s ability to identify seed pods in complex natural environments. In addition, the SPDConv CSP – OmmiKernel (SPOM) module was designed to enhance the model’s detection capability for small alfalfa seed pod targets. By replacing the multi-head attention in the scale feature interaction module (Attention-based cross-scale feature interaction, AIFI) with the convolutional additive token mixer (CATM) module, the model’s understanding of global information was optimized. Additionally, the reparameterized cross-stage partial network with three convolutional layers (RepC3) was optimized by introducing the diversity branch block (DBB) module, and standard convolutions were replaced with linear deformable convolutions during down sampling to improve accuracy. The experimental results showed that the mAP@ 0. 5 of VSC – DETR reached 94. 23%, with average detection time of 88. 3 ms. Compared with RT – DETR, the precision, recall, and mAP@ 0. 5 were improved by 7. 65, 14. 2, and 11. 36 percentage points, respectively, while the average detection time was decreased by 20. 4 ms. This research achievement enabled the quick and accurate identification of alfalfa seed pods of different maturities, providing strong support for selecting high-quality seeds, thereby improving seed germination rates and production efficiency. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Convolution

Controlled terms: Computational efficiency? - ?Germination? - ?Network layers? - ?Principal component analysis? - ?Signal detection

Uncontrolled terms: Alfalfa seeds? - ?Alfalpha seed pod? - ?Field environment? - ?Maturity? - ?Real-time detection? - ?Seed pods? - ?Small target recognition? - ?Small targets? - ?Target recognition? - ?VSC – DETR

Classification code: 716.1 Information Theory and Signal Processing? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?1101.2 Machine Learning? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1105 Computer Networks

Numerical data indexing: Percentage 2.30E+01%, Time 3.00E-03s, Time 4.00E-03s

DOI: 10.6041/j.issn.1000-1298.2025.11.007

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

38. Design and Experiment of Large-scale High-density Square Baler

Accession number: 20254919668177

Title of translation: 大型高密度方草捆打捆机设计与试验

Authors: Zhai, Gaixia (1); Gao, Xiaohong (1); Wang, Zhenhua (1); Wang, Qiang (1); Dai, Xiaojun (1); Yang, Li (1); He, Gang (1)

Author affiliation: (1) Hohhot Branch of Chinese Academy of Agricultural Mechanization Sciences Co., Ltd., Hohhot; 010010, China

Corresponding author: He, Gang(hegang001@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 122-134

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In view of the efficient operation of forage and crop straw picking and baling, a large-scale high-density square baler was designed, and the overall structure and working principle of the whole machine were expounded. The double-side limited rotary curved surface track type tine pickup was designed, its main technical parameters were determined, the motion characteristics and the interaction between the material and the elastic tooth at each stage of pickup were analyzed, and the picking effect was verified based on the multi-body dynamics-discrete element coupling simulation. The chopping feeding device with center symmetric V-shaped installation and axial spiral distribution was designed. The blade on the cutting tool holder cooperated with the forced feeding roller fork group in interval insertion to complete the cutting and feeding at one time. To prevent material blockage, the phase difference between the cutting blade and the fork was 120°. The feeding filling and pre-pressing device was designed, and the movement of the crank feeding mechanism and the filling feeding mechanism was analyzed. Based on the rapid return characteristics of the bias crank slider mechanism, a parallel double crank connecting rod compression mechanism was designed. The structure parameters of the compression mechanism and the compression chamber were determined. The transmission system, which comprised one main input route and four branch routes, was laid out. And the operation process and fault monitoring intelligent control system was developed. It realized the efficient and stable operation of the whole process of forage picking, conveying, feeding, pre-pressing filling, compression, knotting and unloading. The performance test of alfalfa grass strips showed that the baling rate of the equipment reached 100%, the bale density was 250 kg / m3 , and the pure working hour productivity was 26 t / h, and all performance indicators met the requirements of national standards. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Pickups

Controlled terms: Agricultural machinery? - ?Cutting tools? - ?Feeding? - ?Filling? - ?Unloading

Uncontrolled terms: Compress? - ?Compression mechanism? - ?Crop straws? - ?Cut and feed? - ?Feeding mechanism? - ?Forage harvesting? - ?Large-scales? - ?Pressung? - ?Square baler? - ?Whole machine

Classification code: 603.1 Machine Tool Accessories? - ?691.2 Materials Handling Methods? - ?752 Sound Devices, Equipment and Systems? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Linear density 2.50E+02kg/m, Percentage 1.00E+02%

DOI: 10.6041/j.issn.1000-1298.2025.11.011

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

39. Canal System Water Resource Optimization Based on Dynamic Water Diversion Process

Accession number: 20254919628422

Title of translation: 基于动态引水过程的渠系水资源优化配置研究

Authors: Zhang, Chenglong (1, 2); Yuan, Yuan (1, 2); Xu, Haolin (3); Guo, Shanshan (1, 2); Huo, Zailin (1, 2)

Author affiliation: (1) College of Water Resources and Civil Engineering, China Agricultural University, Beijing; 100083, China; (2) State Key Laboratory of Efficient Utilization of Agricultural Water Resources, China Agricultural University, Beijing; 100083, China; (3) School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan; 430060, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 677-686

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The advancement of modern irrigation districts in China is constrained by suboptimal canal system operation management, necessitating enhanced water resource allocation and management to ensure stable agricultural development. Diversion stability served as a critical prerequisite for canal system optimization. Its enhancement minimized gate operation frequency, thereby reducing operational costs and maintenance complexity while improving overall irrigation system efficiency and reliability. Focusing on a representative secondary canal system—the Datan Sub-main Canal in Hetao Irrigation District, an optimized water allocation model targeting diversion stability improvement and seepage loss reduction was established, with net flow rate, start time, and end time of subordinate canal distribution as decision variables. To address the imprecise characterization of dynamic diversion flows in upstream channels inherent in traditional 0 -1 integer programming models, the proposed model replaced discrete time variables with continuous time variables, employing the non-dominated sorting genetic algorithm Ⅱ (NSGA Ⅱ) for solution. Results demonstrated that the baseline model achieved optimized objective values of 2. 47 m3 / s (standard deviation of upstream canal flow) and 1. 684 2 × 106 m3 (total system seepage loss). The enhanced model reduced these to 1. 86 m3 / s and 1. 679 7 × 106 m3 respectively, signifying improved upstream diversion stability and decreased total canal seepage loss. Consequently, the refined model achieved higher accuracy in characterizing dynamic flow processes—particularly in capturing short-term flow fluctuations and corresponding peak values—thereby enhancing diversion stability. This provided a scientific basis for ensuring the reliability and safety of canal distribution schemes. In summary, the optimization outcomes enabled centralized and efficient water distribution, reduced canal seepage losses, and supported policymakers in formulating more rational canal water allocation strategies. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Continuous time systems

Controlled terms: Genetic algorithms? - ?Hydraulic structures? - ?Irrigation? - ?Irrigation canals? - ?Multiobjective optimization? - ?River diversion? - ?Screening? - ?Seepage? - ?System stability

Uncontrolled terms: Canal systems? - ?Continous time? - ?Continuous time variable? - ?Hetao irrigation districts? - ?Multi-objectives optimization? - ?NSGA ⅱ algorithm? - ?Resources optimization? - ?Secondary canal system? - ?Time variable? - ?Water resource optimization ? - ?Waters resources

Classification code: 441 Dams and Reservoirs? - ?444 Water Resources? - ?446 Waterworks? - ?446.1 Water Supply Systems? - ?483 Soil Mechanics and Foundations? - ?802.3 Chemical Operations? - ?821.4 Agricultural Methods? - ?961 Systems Science? - ?1106 Computer Software, Data Handling and Applications? - ?1201.7 Optimization Techniques

Numerical data indexing: Size 2.00E+06m, Size 4.70E+01m, Size 7.00E+06m, Size 8.60E+01m

DOI: 10.6041/j.issn.1000-1298.2025.11.066

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

40. Lightweight Tomato Leaf Disease Detection Method Based on Improved YOLO v8n

Accession number: 20255019668828

Title of translation: 基于改进 YOLO v8n 的轻量化番茄叶片病害检测方法

Authors: Zhang, Jing (1); Hua, Wen (1); Liu, Xiaomei (2); Sun, Yueping (1)

Author affiliation: (1) School of Electrical and Information Engineering, Jiangsu University, Zhenjiang; 212013, China; (2) Jiangsu Kemao Information Technology Co., Ltd., Zhenjiang; 212000, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 538-549

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of overlapping tomato leaves with shading in greenhouse environment, small spots of some diseases leading to low detection accuracy, and large number of parameters of the existing detection model, which is difficult to be deployed to the embedded end with limited computational resources, a lightweight tomato leaf disease detection method based on the improvement of YOLO v8n was proposed. Firstly, the HGNetV2 network was used to replace the backbone network of the YOLO v8n model, and a lightweight convolutional GhostConv structure was introduced to improve the extraction of multi-scale disease features while reducing the number of parameters and computation of the model. Secondly, an improved C2f_Star module was used in the neck network part to achieve further lightweighting while enhancing the feature representation of the model. Then the lightweight attention mechanism SimAM module was added to strengthen the feature extraction ability of the model for small lesions. Finally, the hybrid loss function Wise_Focaler_MPDIoU was designed to replace the original loss function to improve the performance of the network bounding box regression and accelerate the convergence speed. The experimental results showed that the number of parameters and model size of the improved model (GSSW YOLO v8n) were reduced by 28. 9% and 26. 9%, respectively, compared with the original model, while the average detection accuracy on the tomato leaf disease dataset used reached 97. 4%, which was an improvement of 3. 0 percentage points, comparing with other target detection algorithms of Faster R CNN, YOLO v5n, YOLO v6n, and YOLO v7 tiny by 13. 3, 5. 4, 9. 9, and 9. 2 percentage points, respectively. The improved model (GSSW YOLO v8n) had high detection accuracy and less number of parameters, which can provide a reference for the deployment and application of the tomato leaf disease detection model on the embedded device side. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Object detection

Controlled terms: Extraction? - ?Feature extraction? - ?Fruits? - ?Green computing? - ?Object recognition? - ?Plant diseases

Uncontrolled terms: Detection accuracy? - ?Detection methods? - ?Detection models? - ?Leaf disease detections? - ?Lightweighting? - ?Objects detection? - ?SimAM? - ?Tomato disease? - ?Tomato leaf? - ?YOLO v8

Classification code: 103 Biology? - ?802.3 Chemical Operations? - ?821.5 Agricultural Products? - ?1101.2 Machine Learning? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1501 Sustainability

Numerical data indexing: Percentage 4.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.052

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

41. Design and Experiment of Alfalfa Picking, Baling and Wrapping Integrated Machine in Hilly and Mountainous Areas

Accession number: 20254919668201

Title of translation: 丘陵山区苜蓿捡拾打捆裹包一体机设计与试验

Authors: Zhang, Keping (1); Li, Shengsheng (1); Li, Xiaokang (2); An, Jing (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou; 730070, China; (2) Gansu Academy of Mechanical Sciences Co., Ltd., Lanzhou; 730030, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 135-145

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issue of high difficulty in mechanical silage harvesting of alfalfa and high loss rate of bales in hilly and mountainous areas, an alfalfa picking, baling and wrapping integrated machine was designed, based on the topography of hilly and mountainous areas in Gansu Province and the agronomic requirements for alfalfa silage. The machine simultaneously performed picking, shredding, baling, net wrapping, and film wrapping operations on windrowed alfalfa. The pickup device, utilizing a swing roller and pusher disc cam mechanism as its key component, featured a working width of 0. 7 m, a tine spacing of 58 mm, and a rotational speed of 124 r/min. The fixed blades and moving blades of the shredding device collaboratively achieved alfalfa shredding and forced feeding, the sliding shredding angle was 40°, the number of fixed blades was 13, and the feeding roller speed was 300 r/min. The baling device was mainly composed of the opening cylinder, drum, gap sensing device and frame, the alfalfa was rolled into a cylindrical bale with a diameter of 500 mm and a height of 700 mm by the baling chamber formed with 10 drums arranged in a vortex pattern. The speed ratio of the support roller and the wrapping arm in the film wrapping device was set at 0. 55, the gear ratio of the film-guide differential unit was 1. 5, and the film stretch ratio was 50% . The whole machine field performance experiment was conducted on alfalfa fields in hilly and mountainous areas with an undulating slope ranging from 0° to 15°, the results showed that when the operating speed was 1. 3 ~ 1. 4 m/s and the density of windrowed alfalfa was approximately 1. 5 kg/m, the bale density was 566. 02 kg / m3 , with a total loss rate of 3. 67%, meeting the requirements of alfalfa wrapping silage. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Pickups

Controlled terms: Agricultural machinery? - ?Packaging? - ?Rollers (machine components)? - ?Topography

Uncontrolled terms: Alfalpha? - ?Baling and wrapping? - ?Film wrapping? - ?Film-wrapping device? - ?Gansu province? - ?Hilly and mountainous areas? - ?Integrated machines? - ?Loss rates? - ?Mechanical? - ?Pickup device

Classification code: 214 Materials Science? - ?601.2 Machine Components? - ?694 Packaging? - ?752 Sound Devices, Equipment and Systems? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Percentage 6.70E+01%, Size 5.00E-01m, Size 5.80E-02m, Size 7.00E+00m, Size 7.00E-01m, Velocity 4.00E+00m/s, Angular velocity 2.0708E+00rad/s, Angular velocity 5.01E+00rad/s, Linear density 2.00E+00kg/m, Linear density 5.00E+00kg/m, Percentage 5.00E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.012

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

42. Calibration and Experiment of Discrete Element Model Parameters for Alfalfa Seed Pod Separation and Cleaning

Accession number: 20254919668195

Title of translation: 苜蓿种荚分离清选离散元模型参数标定与试验

Authors: Zhang, Qing (1, 2); Sun, Lihao (1); Fan, Xiangdong (3); Ma, Chenghao (1); Hu, Jianliang (4); Liu, Jinlong (2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Forage Seed Production and Processing Machinery and Equipment, Ministry of Agriculture and Rural Affairs, Shizuishan; 753400, China; (3) Shizuishan Animal Husbandry and Aquatic Products Technology Promotion and Service Center, Shizuishan; 753000, China; (4) Shijiazhuang Xinnong Machinery Co., Ltd., Shijiazhuang; 052400, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 156-164

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to enhance the application of the discrete element method (DEM) in investigating the dynamic separation and cleaning process of alfalfa seed pods following mechanical combing detachment, and improve the accuracy of DEM-based simulations, a study was conducted to calibrate the parameters of a helical-curled alfalfa seed pod DEM model by using combination of experimental and simulation approaches. The static friction coefficient, rolling friction coefficient, and restitution coefficient for pod-pod and pod-steel plate interactions were measured by using the inclined plane method and the flat plate collision method. The actual repose angle of alfalfa pods was determined as 33. 19° via the funnel drop method. A discrete element model of spiral-curled alfalfa pods was then developed by using a multisphere fast-packing approach in EDEM. Sensitivity analysis via Plackett - Burman experimental design identified four parameters with significant effects on the repose angle: Poisson’s ratio, pod-pod static friction coefficient, pod-pod rolling friction coefficient, and pod-steel plate static friction coefficient. These parameters were further optimized through steepest ascent experiments and central composite design (CCD), yielding optimal values of 0. 200, 0. 501, 0. 500, and 0. 218, respectively. The maximum relative error between the simulated and experimental repose angles was 1. 2%, validating the calibration accuracy. DEM simulations of the pod separation and cleaning process were subsequently performed. Field experiments confirmed the reliability of the calibrated parameters, with the maximum relative error in separation rate of 6. 76% . The research result can provide a robust methodological framework for DEM-based analysis and optimization of alfalfa seed pod separation systems, offering critical parameter references for future research and engineering applications. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Stiction

Controlled terms: Calibration? - ?Cleaning? - ?Design of experiments? - ?Discrete element methods? - ?Finite difference method? - ?Principal component analysis? - ?Sensitivity analysis? - ?Tribology

Uncontrolled terms: Alfalfa seeds? - ?Alfalpha seed pod? - ?Discrete element models? - ?Discrete elements method? - ?Parameters calibrations? - ?Repose angles? - ?Seed pods? - ?Separation and cleaning? - ?Separation process? - ?Static friction coefficient

Classification code: 601 Mechanical Design? - ?606 Lubrication and Tribology? - ?802.3 Chemical Operations? - ?901.3 Engineering Research? - ?904 Design? - ?1101.2 Machine Learning? - ?1201 Mathematics? - ?1201.5 Computational Mathematics? - ?1201.9 Numerical Methods

Numerical data indexing: Percentage 2.00E+00%, Percentage 7.60E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.014

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

43. Detection of Hail-damaged Fallen Fragrant Pears from UAV Viewpoint Based on Improved YOLO v12

Accession number: 20254919667841

Title of translation: 基于改进 YOLO v12 的无人机视角下冰雹灾后香梨落果检测方法

Authors: Zhang, Yan (1); Zhu, Yali (2); Gao, Jian (2); Zhang, Jinglu (2); Ning, Jifeng (1, 3); Zhang, Huifang (2)

Author affiliation: (1) College of Information Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Institute of Resource and Information, Xinjiang Academy of Forestry Science, Urumqi; 830000, China; (3) Shaanxi Engineering Research Center of Agricultural Information Intelligent Perception and Analysis, Shaanxi, Yangling; 712100, China

Corresponding author: Zhang, Huifang(396930128@qq.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 453-460

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate detection of quantity and distribution information of fallen fruits after disasters is of great significance for the rapid assessment of agricultural fruit insurance. In view of the unstructured orchard environment, the aerial images obtained by the UAV showed that the pear fruit target was small, the color was similar to the fruit leaves, and it was easy to be blocked by ground debris, thus a detection method was proposed based on improved YOLO v12 model. Building on the YOLO v12 model, a coordinate attention (CA) attention module was embedded in the feature extraction layer to enhance the model’s capability to extract features of small-scale fallen fruit targets. A bidirectional weighted feature pyramid (BiFPN) structure was employed in the neck network to optimize the representation ability of low-layer high-resolution features for small targets through cross-scale feature bidirectional fusion and adaptive weight distribution, alleviating the problem of missed detection of pear fruits due to insufficient resolution. Finally, the ShapeIoU loss function was introduced to dynamically adjust feature weights, improving the boundary regression accuracy for irregularly shaped fallen fragrant pear fruit. Experimental results showed that the improved YOLO v12 achieved an mean average precision (mAP@ 0. 5), precision and recall rates of 94. 9%, 88. 8% and 87. 6% respectively on the fallen fragrant pear dataset, outperforming that of YOLO v7 - tiny, YOLO v8n, YOLO v9s, YOLOv 10n, YOLO 11n and YOLO v12n models. The research result demonstrated that the improved YOLO v12 model exhibited superior detection performance for hail-damaged fallen fragrant pears, accurately identifying dense, small-sized, and partially occluded fallen fruit targets in complex orchard environment, providing technical support for disaster loss assessment of fragrant pears in complicated settings. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 21

Main heading: Scales (weighing instruments)

Controlled terms: Aircraft detection? - ?Antennas? - ?Disasters? - ?Feature extraction? - ?Fruits? - ?Orchards? - ?Precipitation (meteorology)? - ?Unmanned aerial vehicles (UAV)

Uncontrolled terms: Aerial images? - ?Attention mechanisms? - ?Coordinate attention attention mechanism? - ?Drop detection? - ?Fragrant pear? - ?Fruit drop detection? - ?Hail disaster? - ?Pear fruit? - ?Rapid assessment? - ?YOLO v12

Classification code: 435.2 Tracking and Positioning? - ?443.3 Precipitation? - ?652.1 Aircraft? - ?716.2 Radar Systems and Equipment? - ?716.5.1 Antennas? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?914 Safety Engineering? - ?942.1.7 Special Purpose Instruments? - ?1101.2 Machine Learning

Numerical data indexing: Percentage 6.00E+00%, Percentage 8.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.043

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

44. Recognition Method for Classification of Potatoes and Impurities Based on SAM CenterNet

Accession number: 20254919668127

Title of translation: 基于 SAM CenterNet 的马铃薯与杂质分类识别方法

Authors: Zhang, Yang (1); Liu, Faying (2); Yang, Zhenyu (1, 3); Wen, Yongshuang (1); Geng, Liangyue (1); Wei, Zhongcai (4); Li, Xueqiang (3)

Author affiliation: (1) School of Mechanical Engineering, Shandong University of Technology, Zibo; 255000, China; (2) School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo; 255000, China; (3) Shandong Intelligent Engineering Technology Research Center of Potato Production Equipment, Dezhou; 253600, China; (4) School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo; 255000, China

Corresponding author: Yang, Zhenyu(05338@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 480-489

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to address the problem that impurities (soil and stones) need to be identified and rejected in the process of potato harvesting and warehousing, an improved potato impurity classification and identification method based on CenterNet was proposed. Firstly, a six-channel potato impurity recognition and impurity removal device was built to capture the source images. Secondly, in the feature extraction stage of the backbone network, the traditional convolutional downsampling was replaced with SPD Conv downsampling to improve the recognition network’s ability to recognize the fine-grained recognition of potatoes and impurities at different scales, and solve the problem of missed recognition due to the existence of feature similarity between soil and stone blocks and some potatoes. Then, in order to suppress the background invalid features, the ACmix attention mechanism was added to enhance the effective features on the potato and impurity surfaces, and to solve the problem of mud adhering to the potato surface and the image background leading to misrecognition. Finally, a multilevel feature fusion structure was constructed to obtain richer texture feature information of the potato and impurity surfaces, and solve the problem of low recognition accuracy due to the single output feature of the model. The potato and impurity image dataset was constructed to test the improved potato and impurity classification and recognition method, and the test results showed that the number of model parameters of the improved algorithm was 1. 013 × 107 , the forward inference time was 27 ms, and the average accuracy mean value was 97. 5% . Compared with the original CenterNet model, the number of parameters was increased by 1. 66 × 106 , the forward inference time was increased by 2 ms, but the mean average precision was increased by 8. 2 percentage points. In the process of potato harvest and storage, the method of potato and impurity classification can meet the technical requirements of impurity identification and removal. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Impurities

Controlled terms: Classification (of information)? - ?Deep learning? - ?Feature extraction? - ?Image enhancement? - ?Inference engines? - ?Removal? - ?Statistical tests? - ?Textures

Uncontrolled terms: Acmix? - ?Classification and identifications? - ?Classification methods? - ?Deep learning? - ?Down sampling? - ?Identification method? - ?Impurity removal? - ?Potato? - ?Recognition methods? - ?SAM centernet

Classification code: 214 Materials Science? - ?716.1 Information Theory and Signal Processing? - ?802.3 Chemical Operations? - ?903.1 Information Sources and Analysis? - ?1101.1 Expert Systems? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1106.3.1 Image Processing? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 5.00E+00%, Time 2.00E-03s, Time 2.70E-02s

DOI: 10.6041/j.issn.1000-1298.2025.11.046

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

45. Automatic Feed Throwing and Loading System for Silage Harvester

Accession number: 20255019701314

Title of translation: 青贮饲料收获机自动抛料装车系统研究

Authors: Yu, Yongfeng (1, 2); Li, Leixia (1, 2); Yang, Zeyu (1, 2); Yang, Deqiu (1, 2); He, Zhifei (1, 2); Teng, Shaomin (1, 2); Hua, Rongjiang (1, 2); Xie, Shengjian (1, 2)

Author affiliation: (1) Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing; 100083, China; (2) State Key Laboratory of Agricultural Equipment Technology, Beijing; 100083, China

Corresponding author: Li, Leixia(lileixia1016@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 63-75

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the issues of high labor intensity and low automation levels in the manual operation of the feed loading process during field operations of silage harvesters, the automatic feed throwing and loading system for silage harvesters was designed. By analyzing the accumulation model of silage feed being discharged into the hopper of a following feed truck, a feed landing point positioning method and loading rules was proposed based on hopper dimensions. Subsequently, a discrete element model was established based on the material properties of silage feed and simulated by using EDEM software. The results indicated that the proposed discharge method achieved more uniform loading under various discharge angles, thereby improving the utilization rate of the hopper space. The actual feed throwing trajectory of silage feed was simplified, treating the throwing arm and the entire feed throwing mechanism as a special five-degree-of-freedom serial robotic arm. The improved Denavit Hartenberg modeling method was used to establish a link coordinate system, and forward and inverse kinematic analyses were conducted on the throwing arm. The target values for joint variables required to reach the ideal landing point were obtained. By controlling the actual joint variable values to approach the target values, automatic feed loading during silage harvesting was achieved. Field trials were conducted by using joint angle error, filling rate of the truck, and feed throwing loss rate as experimental indicators. The results showed that the average absolute errors for the rotation angle of the throwing arm base and the rotation angle of the throwing head were 1.80° and 1.08°. The filling rate of the truck in the automatic feed throwing and loading system was 4.94 percentage points higher than that of the manually controlled feed loading system. And the average feed throwing loss rate was reduced by 0.45 percentage points compared with that of the manually controlled feed loading method, meeting the requirements for field harvesting. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Control systems

Controlled terms: Degrees of freedom (mechanics)? - ?Harvesters? - ?Hoppers? - ?Loading? - ?Process control? - ?Robotic arms? - ?Trucks

Uncontrolled terms: Automatic feed throwing and loading? - ?Automatic feeds? - ?Discrete elements? - ?Feed loading? - ?Filling rate? - ?Landing points? - ?Loading system? - ?Loss rates? - ?Silage harvesters? - ?Target values

Classification code: 101.6.1 Robotic Assistants? - ?663.1 Heavy Duty Motor Vehicles? - ?691 Bulk Handling and Unit Loads? - ?691.2 Materials Handling Methods? - ?731 Automatic Control Principles and Applications? - ?731.1 Control Systems? - ?731.5 Robotics? - ?821.2 Agricultural Machinery and Equipment? - ?913.3 Quality Assurance and Control? - ?1301.1.1 Mechanics

DOI: 10.6041/j.issn.1000-1298.2025.11.005

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

46. Semi-supervised Crop Remote Sensing Classification Based on Ensemble and Confident Learning

Accession number: 20254919628463

Title of translation: 基于集成和置信学习的半监督作物遥感分类方法

Authors: Che, Hongyan (1, 2); Pan, Yaozhong (1, 3); Xia, Xingsheng (1, 4); Wang, Lin’gang (1, 4); Huang, Yongsheng (1, 4); Chen, Qiong (1, 4)

Author affiliation: (1) Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining; 810016, China; (2) School of Agriculture and Forestry Economics and Management, Lanzhou University of Finance and Economics, Lanzhou; 730020, China; (3) State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing; 100875, China; (4) School of Geographical Sciences, Qinghai Normal University, Xining; 810016, China

Corresponding author: Xia, Xingsheng(xxs@qhnu.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 418-432

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Crop distribution mapping is pivotal in modern agricultural management, providing a scientific basis for crop yield prediction and precision agriculture decision-making. Despite its significant potential in crop classification, the practical application of deep learning is still limited by the cost of sample acquisition and classification accuracy. An ensemble learning and confident learning based semi-supervised classification framework (ECL SSCF) was proposed, which aimed to achieve high-precision crop mapping with a small number of ground-truth samples. The ECL SSCF consisted of two modules: ensemble learning and confident learning. The ensemble learning module improved classification accuracy by training multiple deep learning models in parallel and integrating their classification results through a plurality voting method. The confident learning module evaluated the confidence of pseudo-samples based on the model’s predictive probabilities and selected high-confidence pseudo-samples to expand the training set, thus overcoming overfitting caused by small sample sizes. Crop classification results in the two study areas, Zhijiang city and Huantai county in China, showed that by introducing the confident learning strategy, the overall accuracy of pseudo-samples in both study areas was improved by 17. 2 percentage points and 9. 3 percentage points, respectively, surpassing single deep learning models such as LSTM, Conv1D, ConvLSTM, and TempCNN. By training four deep learning models with purified pseudo-samples and integrating their results, ECL SSCF achieved an overall classification accuracy of 90. 4% and 86. 3% in the two study areas, significantly outperforming any single deep learning model. To more comprehensively evaluate the performance of ECL SSCF, the impact of the quantity and quality of its generated pseudo-samples on the predictive performance of five models were further explored. The experimental results indicated that ECL SSCF demonstrated robust performance when the sample size reached approximately 30% of the standard sample set and the proportion of noisy samples was below 30% of the standard sample set. This suggested that the combination of ensemble learning and confident learning strategies endowed ECL SSCF with high-quality samples and stable classification performance, alleviating the dependence of deep learning models on a large number of manually labeled samples and providing a reference scheme for large-scale remote sensing crop mapping. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Remote sensing

Controlled terms: Classification (of information)? - ?Contrastive Learning? - ?Crops? - ?Decision making? - ?Deep learning? - ?Federated learning? - ?Learning algorithms? - ?Learning systems? - ?Mapping? - ?Precision agriculture ? - ?Self-supervised learning? - ?Semi-supervised learning

Uncontrolled terms: Classification accuracy? - ?Classification framework? - ?Confident learning? - ?Crop classification? - ?Ensemble learning? - ?Learning models? - ?Remote-sensing? - ?Semi-supervised learning? - ?Semisupervised classification (SSC)? - ?Study areas

Classification code: 405.3 Surveying? - ?716.1 Information Theory and Signal Processing? - ?731.1 Control Systems? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?903.1 Information Sources and Analysis? - ?912.2 Management? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning

Numerical data indexing: Percentage 3.00E+00%, Percentage 3.00E+01%, Percentage 4.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.040

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

47. Research on Detection Method of Apple Inflorescence Status of Chemical Flower Thinning Based on ESW YOLO

Accession number: 20254919633878

Title of translation: 基于 ESW YOLO 的化学疏花苹果花序状态检测方法研究

Authors: Chen, Jun (1); Cao, Xiaoming (1); Liu, Guangyao (1); Hu, Guangrui (2); Zhang, Xinyue (1); Wen, Shiwei (1)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) School of Design, Xi’an Technological University, Xi’an; 710021, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 433-440

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to improve the detection accuracy of small apple inflorescences during the chemical thinning period in complex orchard environments and enhance overall detection performance, an improved YOLO v7 tiny based model named EMA and Slim-neck and WIoU YOLO (ESW YOLO) was proposed. The backbone network, neck network, and loss function of YOLO v7 tiny were modified. An efficient multi-scale attention (EMA) module was introduced to replace some convolutional layers in the backbone, enabling the model to focus more effectively on inflorescences while ignoring complex backgrounds. The GSConv Slim neck paradigm was incorporated into the neck network, reducing computational costs without compromising accuracy. Additionally, a dynamic non-monotonic focusing mechanism, wise intersection of union (WIoU), was adopted to address challenges in detecting small, overlapping, and occluded objects, thereby improving detection precision. Experimental results demonstrated that the proposed model achieved precision (P) of 83. 02%, recall(R) of 82. 04%, and mean average precision (mAP) of 86. 83%, representing increases of 2. 62, 3. 74, and 3. 43 percentage points respectively compared with that of the YOLO v7 tiny model, while reducing the number of parameters by 5. 48% . ESW YOLO outperformed YOLO v5s, YOLO v7 tiny, and YOLO v8s in all metrics. Furthermore, in detection tasks under different lighting conditions (front light, back light) and for different apple varieties (Red fuji, Ruixue, Ruixianghong), ESW YOLO consistently achieved F1 scores higher than YOLO v7 tiny, with all detection examples scoring above 84% . The Score CAM method was used to visualize the heatmaps of the output layers in the model’s backbone and neck networks, enhancing the interpretability of the model. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Flow visualization

Controlled terms: Chemical detection? - ?Complex networks? - ?Fluorescence? - ?Fruits? - ?Object recognition

Uncontrolled terms: Apple inflorescence? - ?Chemical flower thinning? - ?Chemical thinning? - ?Detection accuracy? - ?Detection methods? - ?Detection performance? - ?Multi-scales? - ?Objects detection? - ?Thinnings? - ?YOLO v7 tiny

Classification code: 301.1 Fluid Flow? - ?741.1 Light/Optics? - ?802 Chemical Apparatus and Plants; Unit Operations; Unit Processes? - ?821.5 Agricultural Products? - ?902.1 Engineering Graphics? - ?1105 Computer Networks? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 2.00E+00%, Percentage 4.00E+00%, Percentage 4.80E+01%, Percentage 8.30E+01%, Percentage 8.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.041

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

48. Tracking Method for Litopenaeus vannamei Shrimp Larvae Based on Improved YOLO v8

Accession number: 20255019669057

Title of translation: 基于改进 YOLO v8 的南美白对虾虾苗跟踪方法

Authors: Chen, Ming (1, 2); Gan, Dongmei (1, 2); Lu, Peng (1, 3); Gu, Hao (1, 2); Li, Zheng (2, 4)

Author affiliation: (1) College of Information Technology, Shanghai Ocean University, Shanghai; 201306, China; (2) Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai; 201306, China; (3) Laboratory for Marine Surveying and Mapping with Intelligent Analysis, Shanghai; 201306, China; (4) Shanghai Shilin Information Technology Co., Ltd., Shanghai; 201306, China

Corresponding author: Lu, Peng(plu@shou.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 599-611

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The intelligent automation of shrimp larvae farming demonstrates great potential in improving farming efficiency and reducing labor costs. Tracking technology for shrimp larvae is of significant value for in-depth research on their vitality and quality. Due to the small size, semi-transparency, and high density of shrimp larvae, traditional methods often face many challenges: small targets are easily missed, detection accuracy is low under high density, and tracking errors are high due to the similar appearance and fast movement of shrimp larvae. To address these issues, focusing on P15 stage Pacific white shrimp larvae, a tracking method was proposed based on an improved YOLO v8. Firstly, an improved YOLO v8 network was selected as the detector, with CARAFE replacing YOLO v8’s Upsample layer. CARAFE’s adaptive upsampling preserved the semi-transparent edges and morphological features of shrimp larvae, reducing distortion. The backbone network was optimized by using the BiFormer attention mechanism and the C2f BFB module, which enhanced precision in handling small targets while keeping the model lightweight. Finally, the original model’s CIoU loss was replaced with the Wise Focaler ShapeIoU loss function, further improving detection accuracy. A matching strategy that combined distance and angle matching algorithms was also proposed, and the pyramid LK optical flow method was introduced, constructing an accurate and stable tracker that effectively addressed tracking errors caused by the fast movement and similar appearance of shrimp larvae. Experimental results showed that the improved YOLO v8 achieved a 1. 1 percentage points increase in mAP@ 0. 5 and a 2. 9 percentage points increase in recall rate compared with that of the original model, significantly reducing missed detections and improving detection accuracy. The proposed tracker achieved a MOTA of 74. 52%, MOTP of 75. 62%, and IDF1 of 76. 42% . Compared with DeepSORT, SORT, and ByteTrack algorithms, MOTA was significantly improved, demonstrating excellent tracking performance. The research result can provide strong technical support for the automation of shrimp larvae farming and offer ideas and methods for related research fields. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Shellfish

Controlled terms: Automation? - ?Error detection? - ?Target tracking? - ?Wages

Uncontrolled terms: Detection accuracy? - ?Lucas-kanade? - ?Missed detections? - ?Multi-target-tracking? - ?Optical flow methods? - ?Pyramid luca kanade optical flow method? - ?Shrimp larva? - ?Small targets? - ?Tracking method? - ?YOLO v8

Classification code: 103 Biology? - ?435.2 Tracking and Positioning? - ?731 Automatic Control Principles and Applications? - ?731.1.1 Error Handling? - ?912.3 Personnel

Numerical data indexing: Percentage 4.20E+01%, Percentage 5.20E+01%, Percentage 6.20E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.058

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

49. Optimal Power-Load Matching and Energy Consumption Verification of Agricultural Multi-rotor UAVs

Accession number: 20255019669059

Title of translation: 农用多旋翼无人机动力 载荷最优匹配与能耗验证

Authors: Chen, Yu (1); Zhang, Zhixun (1); Shen, Suiyuan (1); Huang, Taoran (1); Chen, Haoxuan (1); Li, Jiyu (1)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China

Corresponding author: Li, Jiyu(lijiyu@scau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 339-348

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to solve the problem of load configuration for optimal dynamic energy consumption of agricultural multi-rotor UAVs, a method of optimizing the dynamic energy consumption for agricultural UAVs by load configuration according to their power systems and number of rotors was proposed. Firstly, on the basis of established dynamic energy consumption model for agricultural UAVs, the dynamic energy consumption corresponding to the load of seven different rotor numbers (four-rotor to ten-rotor) under three kinds of motor power system configuration was calculated respectively. Then the load-dynamic energy consumption curves corresponding to different rotor numbers were plotted. And the data processing software was used to ‘intersect’ the curves to get the intersection points of the load-dynamic energy consumption curves under different rotor numbers. It was found that for agricultural UAVs with different rotor numbers for the same power system configuration, the intersection of the load-dynamic energy consumption curves for the two adjacent axis rotor number configurations was the optimal dynamic energy consumption load intersection. The load values corresponding to the intersection points of the optimal dynamic energy consumption load can form the optimal dynamic energy consumption load interval from small to large, which was the optimal load configuration interval corresponding to different rotor numbers. The dynamic energy consumption of the agricultural UAVs can be optimized by using the above method to configure the load according to the number of rotor numbers. Finally, totally 80 flight verification tests of different loads were carried out by using the designed agricultural UAV with four, six and eight rotor structures. The test data showed that there was optimal dynamic energy consumption load configuration interval under different rotor configurations of four, six and eight. Among them, the average error between the test value and the theoretical value of the four-rotor UAV dynamic energy consumption was 3. 22% . The average error of the six-rotor UAV was 2. 87% . The average error of the eight-rotor drone was 2. 85% . Furthermore, through the error analysis of the flight measured dynamic energy consumption value and the theoretical calculated energy consumption value, the optimal dynamic energy consumption load effective zone and failure zone was acquired. And then a more accurate optimal dynamic energy consumption load configuration interval was acquired. The research result can provide a reference for optimizing the load configuration of agricultural multi-rotor UAVs, which had certain theoretical significance and engineering practice value. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Unmanned aerial vehicles (UAV)

Controlled terms: Agriculture? - ?Data handling? - ?Energy utilization? - ?Error analysis? - ?Flight dynamics? - ?Optimization

Uncontrolled terms: Agricultural UAV? - ?Average errors? - ?Dynamic energy consumption? - ?Load configurations? - ?Load dynamics? - ?Optimal dynamic energy consumption? - ?Optimal dynamics? - ?Power? - ?Power system? - ?Rotor number

Classification code: 651 Aerodynamics? - ?652.1 Aircraft? - ?731.1.1 Error Handling? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?1009.2 Energy Consumption? - ?1106.2 Data Handling and Data Processing? - ?1201.7 Optimization Techniques

Numerical data indexing: Percentage 2.20E+01%, Percentage 8.50E+01%, Percentage 8.70E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.032

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

50. Analysis of Transient Entropy Generation Loss Mechanism under Variable Speed Water Pump-turbine Pump Operating Conditions

Accession number: 20254919668120

Title of translation: 可变速水泵水轮机泵工况瞬态熵产损失机理分析

Authors: Dong, Wei (1, 2); Zhang, Haichen (1); Li, Peixuan (1); Fan, Xugang (1)

Author affiliation: (1) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) State Key Laboratory of Hydro-power Equipment, Harbin; 150040, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 349-358

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The variable-speed pump-turbine can maintain a relatively stable operating state at different speeds during operation under pump conditions by precisely adjusting the speed. This adjustment method enables the pump-turbine to better adapt to different water flow conditions and load requirements, thereby improving its operational efficiency and expanding its operating range. During the pre-processing setup in the computational fluid dynamics (CFD) software, the time-varying speeds and flow rates were numerically calculated by using the user defined function (UDF) method. Subsequently, in combination with the entropy generation theory, a deep analysis was conducted on the internal flow field evolution mechanism induced by the transient process of variable-speed regulation under pump conditions of the pump-turbine, investigating the flow loss of variable-speed regulation under pump conditions of the pump-turbine. The results indicated that during the acceleration operation of the pump-turbine, the incidence of stall vortex at the double-row guide vanes was increased while the vortex inside the impeller flow passage remained basically unchanged. The overall trend of entropy generation in the impeller region showed a gradual increase, while the entropy generation at the double-row guide vanes was firstly decreased and then increased. During the deceleration operation of the pump-turbine, the incidence of stall vortex near the outlet edge of the fixed guide vanes was increased, and the entropy generation in the impeller region as well as at the double-row guide vanes was gradually decreased. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Computational fluid dynamics

Controlled terms: Computation theory? - ?Entropy? - ?Flow of water? - ?Hydraulics? - ?Impellers? - ?Speed regulators? - ?Turbine pumps? - ?Turbines? - ?Two phase flow? - ?Vortex flow

Uncontrolled terms: Condition? - ?Entropy generation? - ?Entropy generation theory? - ?Flow loss? - ?Operating condition? - ?Pump operating condition? - ?Pump-turbines? - ?Speed regulation? - ?Variable speed? - ?Variable speed regulation

Classification code: 301.1 Fluid Flow? - ?301.1.1 Liquid Dynamics? - ?301.1.4 Computational Fluid Dynamics? - ?302.1 Thermodynamics? - ?601.2 Machine Components? - ?609.2 Pumps? - ?704.2 Electric Equipment? - ?732.1 Control Equipment? - ?1007.1 Turbines and Steam Turbines? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1401.1 Hydraulics

DOI: 10.6041/j.issn.1000-1298.2025.11.033

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

51. Soil Physical Properties and Grass Yield under Different Root Cutting and Punching Parameters in Yili Mountain Meadow

Accession number: 20254919668182

Title of translation: 不同切根打孔参数条件下伊犁山地草甸土壤物理性质与产草量研究

Authors: Fan, Xueting (1, 2); Zhang, Lian (3); He, Haixiu (3); Chen, Ning (3); Duan, Zhenyu (3); Liang, Fei (1, 2)

Author affiliation: (1) College of Resources and Environment, Yili Normal University, Yining; 835000, China; (2) Laboratory of Agricultural Resources and Environment in Yili River Valley, Yili Normal University, Yining; 835000, China; (3) Department of Science and Technology Services and Achievements Transformation, Xinjiang Academy of Agricultural Reclamation Science, Shihezi; 832000, China

Corresponding author: Liang, Fei(liangfei3326@126.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 165-176

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to investigate the effects of root cutting and punching combinations on soil improvement and grass yield in mountain meadow soils, a targeted experiment was conducted in the Yili River Valley from May 2023 to September 2024. The experimental factors included root cutting depths (5 cm, 10 cm, 15 cm), punching (20 cm, 40 cm, 60 cm), and a control (CK). The experiment aimed to analyze their influence on the soil’s physical properties and grass yield, providing a comprehensive understanding of how these treatments affect the ecosystem. The results showed that root cutting reduced soil bulk density by 14. 58% to 20. 83%, reduced soil specific gravity by 3. 54% to 9. 73%, increased soil porosity by 2. 76% to 5. 47%, and significantly boosted fresh grass yield by 26. 54% to 36. 01%, and dry grass yield by 18. 46% to 26. 80%, while also reducing soil compaction. Similarly, punching reduced soil bulk density by 16. 67% to 17. 71%, reduced soil specific gravity by 2. 65% to 7. 96%, increased soil porosity by 1. 52% to 5. 56%, and improved fresh grass yield by 20. 68% to 35. 08%, and dry grass yield by 9. 31% to 29. 49%, while also enhancing soil compaction. The combination of root cutting and punching effectively improved soil physical properties and increased grass yield, leading to a better mountain meadow environment. Among the treatments, D60Q5 demonstrated the most significant effects on both soil improvement and yield increase, providing both ecological and economic benefits. In conclusion, the optimal root cutting depth was 5 ~ 10 cm, and the optimal punching spacing was 40 ~ 60 cm. The combination of punching at 60 cm and root cutting at 5 cm provided a sustainable and efficient approach to improve soil physical properties and increase grass productivity, which can be considered as the optimal solution for improving the mountain meadows in the Yili River Valley. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Punching

Controlled terms: Abiotic? - ?Agribusiness? - ?Bulk Density? - ?Compaction? - ?Cutting? - ?Density (specific gravity)? - ?Economic and social effects? - ?Ecosystems? - ?Forestry? - ?Landforms ? - ?Plants (botany)? - ?Porosity? - ?Soil mechanics? - ?Soils

Uncontrolled terms: Cutting depth? - ?Grass yield? - ?Mountain meadow? - ?Reduced soils? - ?River valley? - ?Root cuttings? - ?Soil bulk density? - ?Soil physical property? - ?Soil Porosity? - ?Soils improvement

Classification code: 103 Biology? - ?201.5.2 Metal Forming? - ?481.1 Geology? - ?483.1 Soils and Soil Mechanics? - ?604.1 Metal Cutting? - ?821.1 Woodlands and Forestry? - ?821.4 Agricultural Methods? - ?913.4 Manufacturing? - ?971 Social Sciences? - ?1301.1.2 Physical Properties of Gases, Liquids and Solids? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 1.00E00%, Percentage 3.10E+01% to 2.90E+01%, Percentage 4.60E+01% to 2.60E+01%, Percentage 4.70E+01%, Percentage 4.90E+01%, Percentage 5.20E+01% to 5.00E+00%, Percentage 5.40E+01% to 3.60E+01%, Percentage 5.40E+01% to 9.00E+00%, Percentage 5.60E+01%, Percentage 5.80E+01% to 2.00E+01%, Percentage 6.50E+01% to 7.00E+00%, Percentage 6.70E+01% to 1.70E+01%, Percentage 6.80E+01% to 3.50E+01%, Percentage 7.10E+01%, Percentage 7.30E+01%, Percentage 7.60E+01% to 5.00E+00%, Percentage 8.00E+00%, Percentage 8.00E+01%, Percentage 8.30E+01%, Percentage 9.60E+01%, Size 1.00E-01m, Size 1.50E-01m, Size 2.00E-01m, Size 4.00E-01m, Size 4.00E-01m to 6.00E-01m, Size 5.00E-02m to 1.00E-01m, Size 5.00E-02m, Size 6.00E-01m

DOI: 10.6041/j.issn.1000-1298.2025.11.015

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

52. Reverse Engineering-based Discrete Element Model Construction and Parameter Calibration for Leymus chinensis Seeds

Accession number: 20254919667840

Title of translation: 基于逆向工程的羊草种子离散元模型构建与参数标定

Authors: He, Changbin (1, 2); Du, Chaoyu (1); Wang, Chao (1); Wu, Meihong (1); Ye, Yuanwen (3)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China; (2) Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot; 010018, China; (3) Hubei Yongxiang Agricultural Machinery Equipment Co., Ltd., Anlu; 432015, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 146-155

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to enhance the accuracy and fidelity of the discrete element model (DEM) for Leymus chinensis seeds, and provide a reliable model basis for the design and optimization of seed metering systems and critical components, focusing on the construction of a high-fidelity discrete element model and the calibration of contact parameters for Leymus chinensis seeds, utilizing reverse engineering techniques, a realistic geometric model of Leymus chinensis seeds was developed. Subsequently, by employing the Hentz Mindlin (no slip) particle contact model and the single-sphere filling method, the discrete element simulation model was established. Intrinsic mechanical properties, including triaxial dimensions, thousand-seed weight, bulk density, elastic modulus, and Poisson’s ratio were measured. Collision recovery coefficients and rolling friction coefficients between the Leymus chinensis seeds and PLA plastic plates, as well as Q235 steel plates, were calibrated by using collision tests and inclined plane rolling tests, respectively. Furthermore, critical contact parameters, including the inter-seed collision recovery coefficient, static friction coefficient, and rolling friction coefficient, were identified and optimized by using the steepest ascent experiments followed by a quadratic orthogonal rotation design. Subsequently, these calibrated parameters were validated through bench tests. The results indicated that the discrepancy in seed metering mass flow rate between the simulation and bench tests was less than 3%, and the difference in the coefficient of variation for seed metering quantity per row was less than 5% . The findings can provide a foundational model and parameter support for the simulation analysis of the Leymus chinensis seed metering process and the structural optimization of seed metering devices. Moreover, it further demonstrated the good applicability and high accuracy of the three-dimensional discrete element model of seeds constructed by using reverse engineering techniques. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Structural optimization

Controlled terms: Calibration? - ?End effectors? - ?Mindlin plates? - ?Reverse engineering? - ?Stiction? - ?Tribology

Uncontrolled terms: 3D-scanning? - ?Contact parameters? - ?Discrete element models? - ?Discrete elements? - ?Leymus chinensi seed? - ?Leymus chinensis? - ?Parameters calibrations? - ?Recovery coefficients? - ?Reverse engineering techniques? - ?Seed metering

Classification code: 408.1 Structural Members and Shapes? - ?601 Mechanical Design? - ?606 Lubrication and Tribology? - ?731.5 Robotics? - ?901.3 Engineering Research? - ?1201.7 Optimization Techniques

Numerical data indexing: Percentage 3.00E+00%, Percentage 5.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.013

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

53. Design and Test of Digging-pulling Type Garlic Combine Harvester Based on EDEM MFBD

Accession number: 20254919628472

Title of translation: 基于 EDEM MFBD 的挖拔式大蒜联合收获装置设计与试验

Authors: Hou, Rui (1); Liu, Lupeng (2); Xin, Li (3); Shen, Jingxin (4); Zhou, Kai (2); Hou, Jialin (2); Li, Yuhua (2)

Author affiliation: (1) School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing; 100876, China; (2) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China; (3) Shandong Maria Machinery Co., Ltd., Jining; 272000, China; (4) Shandong Academy of Agricultural Machinery Sciences, Ji’nan; 250100, China

Corresponding author: Li, Yuhua(liyuhua@sdau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 253-263

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of poor soil breaking effect, high pulling resistance, and serious soil accumulation often encountered during the digging and pulling processes of garlic combine harvesters, a digging-pulling combination type garlic combine harvesting device was designed. The overall structure and working principle of this device were elaborated and analyzed. Through dynamic analysis and theoretical calculations, the kinematic physical quantities, force conditions, basic structural parameters, and spatial position relationships of key components were optimized. Using the EDEM MFBD coupled simulation method, a contact model among garlic, soil, and the digging-pulling device was established to identify key factors affecting harvesting performance. Microscopic and macroscopic analyses were conducted on the digging-pulling process to explore the interaction relationships between garlic, soil, and the harvesting device, revealing the mechanism of combined digging and pulling harvesting. Single-factor simulation experiments were performed with machine forward speed, horizontal distance from shovel tip to clamping point, and vibration amplitude of the excavating shovel as experimental factors. Test indicators included the number of broken soil BOND bonds, pulling force on garlic stalks, and soil heave height. A quadratic regression orthogonal rotational composite experiment was then carried out. Results showed that optimal performance occurred at a forward speed of 434. 3 mm / s, horizontal distance of 144. 6 mm, and vibration amplitude of 6. 1 mm, achieving 78 283 broken soil BOND bonds, 20 N pulling force on stalks, and 42. 1 mm soil heave height. To verify the working performance of the optimized digging and harvesting device, field tests were conducted, which met the design requirements. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Shovels

Controlled terms: Combines? - ?Couplings? - ?Harvesters? - ?Harvesting? - ?Soil testing? - ?Soils? - ?Spatial variables measurement

Uncontrolled terms: Combine harvesters? - ?Digging-pulling harvesting device? - ?EDEM MFBD? - ?Forward speed? - ?Garlic combine harvester? - ?Harvesting devices? - ?Mechanism of action? - ?Pulling force? - ?Soil heaves? - ?Vibration amplitude

Classification code: 483.1 Soils and Soil Mechanics? - ?601.2 Machine Components? - ?602.1 Mechanical Drives? - ?605.2 Small Tools, Unpowered? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?941.5 Mechanical Variables Measurements? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Force 2.00E+01N, Size 1.00E-03m, Size 6.00E-03m, Velocity 3.00E-03m/s

DOI: 10.6041/j.issn.1000-1298.2025.11.024

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

54. Hyperspectral Remote Sensing Classification Method for Soil by Combining Spectral Multi-feature and XGBoost Model

Accession number: 20255019668574

Title of translation: 综合光谱多特征与 XGBoost 模型的土壤高光谱遥感分类方法

Authors: Hou, Yi (1, 2); Zhang, Xia (1); Zheng, Haiguang (3); Wang, Yibo (1, 2); Wang, Weihao (1, 2); Xiao, Qing (1)

Author affiliation: (1) Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100101, China; (2) University of Chinese Academy of Sciences, Beijing; 100049, China; (3) Zhangjiakou Agricultural Information Center, Zhangjiakou; 075000, China

Corresponding author: Zhang, Xia(zhangxia@radi.ac.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 612-620

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Soil classification by hyperspectral remote sensing is very important for rapid soil survey and even agricultural regionalization. To address the issues of insufficient utilization of the abundant information in hyperspectral data by single-type spectral features and unbalanced soil samples, a soil hyperspectral remote sensing classification method that integrated multiple spectral features with the eXtreme gradient boosting (XGBoost) algorithm was proposed. The adaptive synthetic sampling (ADASYN) technique was used to balance the number of soil samples. Four types of spectral features, including spectral principal component, spectral absorption characteristics, sensitive bands of organic matter and spectral index of soil texture sensitivity, were extracted. The XGBoost algorithm was used to construct the classification model. It was applied to the classification of dark brown soil, chestnut soil, black soil and chernozem soil in North China. The results showed that compared with only using the original reflectance band, spectral principal component and spectral absorption characteristics, the classification accuracy of XGBoost was improved by 22. 22, 20. 44 and 9. 25 percentage points by comprehensive use of spectral principal component, spectral absorption characteristics, sensitive bands of soil organic matter and spectral index of soil texture sensitivity, respectively. This indicated that combining various spectral characteristics enhanced the difference and separability of soil, and improved the classification accuracy. The classification results of XGBoost model were better than those of support vector machine (SVM) and random forest (RF) model, the overall classification accuracy and Kappa coefficient reached 74. 44% and 0. 62, respectively, and the classification accuracy of chernozem soil reached 87. 18% . The top five spectral features of feature importance included depth of absorption valley at 1 900 nm (DP1 900 nm), center wavelength near 700 nm (L700 nm), depth of absorption valley at 700 nm (DP700 nm), de-envelope slope within 1 340 ~ 1 360 nm range (S1 340 nm_1 360 nm) as well as first-order differential at 1 895 nm (R’1 895 nm); all strongly correlated with clay minerals, organic matter content and water. Among the four spectral indices of soil texture sensitivity, the contribution rate of NDI665 nm_490 nm was the highest. The research results can provide reference for rapid soil classification and mapping with a high accuracy. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Learning systems

Controlled terms: Adaptive boosting? - ?Agricultural machinery? - ?Agriculture? - ?Biological materials? - ?Classification (of information)? - ?Clustering algorithms? - ?Machine learning? - ?Object oriented programming? - ?Principal component analysis? - ?Remote sensing ? - ?Soil surveys? - ?Soils? - ?Textures

Uncontrolled terms: Absorption characteristics? - ?Classification accuracy? - ?Hyperspectral remote sensing? - ?Machine-learning? - ?Principal Components? - ?Reflectance spectroscopy? - ?Soil classification? - ?Spectral absorptions? - ?Spectral feature? - ?Spectroscopy feature

Classification code: 203 Biomaterials? - ?214 Materials Science? - ?405.3 Surveying? - ?483.1 Soils and Soil Mechanics? - ?716.1 Information Theory and Signal Processing? - ?731.1 Control Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?903.1 Information Sources and Analysis? - ?1101.2 Machine Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1106.1 Computer Programming? - ?1301.3 Optics

Numerical data indexing: Percentage 1.80E+01%, Percentage 4.40E+01%, Size 3.40E-07m, Size 3.60E-07m, Size 4.90E-07m, Size 6.65E-07m, Size 7.00E-07m, Size 8.95E-07m, Size 9.00E-07m

DOI: 10.6041/j.issn.1000-1298.2025.11.059

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

55. Design and Experiment of Automatic Rowing System for Corn Harvester Based on Improved PSO – PID

Accession number: 20254919668180

Title of translation: 基于改进 PSO – PID 的玉米收获机自动对行系统设计与试验

Authors: Huang, Yi (1); Ren, Ziyu (1); Zhou, Zhihong (2); Zhou, Tao (1); Guo, Gang (3); Huang, Guoyong (3)

Author affiliation: (1) College of Mechanical and Vehicle Engineering, Changsha University of Science and Technology, Changsha; 410114, China; (2) School of Mechanical Engineering, Hunan Industry Polytechnic, Changsha; 410208, China; (3) Zhonglian Agricultural Machinery Co., Ltd., Changsha; 410013, China

Corresponding author: Zhou, Zhihong(632430930@qq.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 234-242

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Corn harvesters operate with wrong rows and missed cuts, and the poor harvesting quality of corn plants with non-linear growth patterns, thus an automatic alignment system for corn harvesters was designed to meet the needs of practical use. The system mainly consisted of three parts: mechanical row alignment device, steering wheel deflection angle detection device, and steering control system. Among them, the mechanical row alignment device and the steering wheel deflection angle detection device can be based on a pure tracking model of the two-wheeled vehicle model in order to detect the lateral deviation and the current lateral error in real time. In addition, the steering control system used a control method based on improved PSO – PID to adjust the harvester’s travelling route to further eliminate the lateral deviation. Finally, the system was verified by Matlab simulation software analysis and experimented in simulated scenarios. The simulation results showed that compared with the traditional PSO – PID control, the improved PSO – PID control reduced the square wave overshoot by 94. 3%, the sinusoidal maximum error by 95. 5%, and the sinusoidal tracking delay was only 0. 01 s. The results of the experiments in the simulation scenarios showed that the percentage of the operating deviation within the range of ± 20 cm was higher than 80% . When the vehicle speed did not exceed 4 km / h, the deviation of straight line path trajectory can be controlled within ± 20 cm, and the proportion of deviation within ± 20 cm in curve path was higher than 93% . The experimental results showed that the system met the requirements of actual operation and provided technical support for the automation of agricultural harvesting. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Three term control systems

Controlled terms: Alignment? - ?Automobile steering equipment? - ?Harvesters? - ?Harvesting? - ?MATLAB? - ?Proportional control systems? - ?Steering? - ?Vehicle wheels

Uncontrolled terms: Alignment system? - ?Angle detection? - ?Automatic alignment? - ?Automatic alignment system? - ?Corn harvesters? - ?Deflection angles? - ?Improve PSO algorithm? - ?Mechanical? - ?PSO algorithms? - ?Steering wheel deflection

Classification code: 601.1 Mechanical Devices? - ?601.2 Machine Components? - ?662.3 Automobile Components and Materials? - ?731.1 Control Systems? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?1106.5 Computer Applications? - ?1201.5 Computational Mathematics

Numerical data indexing: Percentage 3.00E+00%, Percentage 5.00E+00%, Percentage 8.00E+01%, Percentage 9.30E+01%, Size 2.00E-01m, Size 4.00E+03m, Time 1.00E00s

DOI: 10.6041/j.issn.1000-1298.2025.11.022

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

56. Optimization of Fertilization Control System Using GOHBA- Fuzzy- PID Algorithm

Accession number: 20254919668172

Title of translation: 基于 GOHBA- Fuzzy- PID 算法的施肥控制系统优化研究

Authors: Huang, Yourui (1, 2); Lu, Sen (1); Han, Tao (1); Liu, Quanzeng (1)

Author affiliation: (1) School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan; 232001, China; (2) Anhui Polytechnic University, Wuhu; 232000, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 320-328

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the demand for precise irrigation and fertilization in Chinese herbal medicine cultivation and overcome the limitations of traditional PID controllers, such as large overshoot and slow response, an optimized control strategy that combined the global optimization honey badger algorithm (GOHBA) with a fuzzy PID controller was proposed. In the proposed approach, GOHBA was employed to tune the critical gain parameters of the fuzzy PID controller, thereby enhancing system response speed and stability. To evaluate performance, simulations were conducted under four different flow rate conditions: 0. 5 L / min, 1. 0 L / min, 1. 5 L / min, and 2. 0 L / min. The results of GOHBA - Fuzzy - PID were compared with those of standard PID, conventional Fuzzy - PID, and HBA Fuzzy - PID controllers. Simulation outcomes demonstrated that the GOHBA - Fuzzy - PID achieved consistently smaller maximum overshoot (ranging from 16. 7% to 26. 3%) and shorter or comparable settling times (92 ~ 97 s) across all flow rates. Notably, at a flow rate of 2. 0 L / min, the overshoot was reduced to only 18. 2%, which was significantly lower than that obtained by traditional algorithms. These findings indicated that the proposed algorithm exhibited strong robustness and promising applicability for nonlinear and time-varying systems, thereby providing a feasible solution for precision irrigation and fertilization in Chinese herbal cultivation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Water

Controlled terms: Controllers? - ?Cultivation? - ?Electric control equipment? - ?Global optimization? - ?Irrigation? - ?Medicine? - ?Plant extracts? - ?Proportional control systems? - ?Three term control systems? - ?Time varying systems

Uncontrolled terms: Fertilisation? - ?Fertilizer integration;? - ?Fuzzy? - ?Fuzzy PID controller? - ?Fuzzy-PID? - ?Global optimisation? - ?Global optimization honey badger algorithm? - ?PID Algorithm? - ?PID algorithm;? - ?Precision fertilizations

Classification code: 102.1 Medicine? - ?103 Biology? - ?704.2 Electric Equipment? - ?731.1 Control Systems? - ?732.1 Control Equipment? - ?821.4 Agricultural Methods? - ?961 Systems Science? - ?1201.7 Optimization Techniques

Numerical data indexing: Percentage 2.00E+00%, Percentage 3.00E+00%, Percentage 7.00E+00% to 2.60E+01%, Time 9.20E+01s to 9.70E+01s, Volume 0.00E00m3, Volume 5.00E-03m3

DOI: 10.6041/j.issn.1000-1298.2025.11.030

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

57. Design and Experiment of Uniform Distribution System for Plasma Treatment of Alfalfa Seeds

Accession number: 20254919668184

Title of translation: 面向等离子体苜蓿种子处理的均匀布料系统设计与试验

Authors: Hui, Yunting (1, 2); Xi, Junhui (1, 2); Wang, Junwu (3); Wang, Shouyan (1, 2); Shao, Guohu (3); Li, Baicheng (3); Huang, Chen (1, 2); Wang, Decheng (1, 2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Forage Seed Production and Processing Machinery and Equipment, Ministry of Agriculture and Rural Affairs, Shizuishan; 753400, China; (3) Gansu Ok Agricultural Products Drying Equipment Engineering Research Institute Co., Ltd., Lanzhou; 730070, China

Corresponding author: Wang, Decheng(wdc@cau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 184-193

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the core requirement for seeds to pass through the electrode zone in a uniform monolayer during dielectric barrier discharge plasma seed treatment, a multi-stage integrated uniform seed distribution system was developed to resolve issues such as clogging, adhesion, and uneven distribution frequently observed in small-sized seeds like alfalfa during transportation. The system achieved initial seed dispersion via a screw metering device and utilized a dispersing vibrator combined with a multi-channel leveling vibratory conveyor to realize efficient multi-stage homogenization and stable monolayer arrangement of seeds. This approach significantly enhanced the uniformity and consistency of seed distribution, effectively overcoming the problem of uneven plasma treatment caused by seed overlap and accumulation in conventional feeding systems. The research integrated theoretical modeling, dynamic analysis, high-speed imaging, and multi-objective parameter optimization to determine critical structural parameters and establish the threshold conditions for avoiding seed throwing movement during vibration conveyance. It systematically revealed the influence mechanism of excitation voltage on individual and collective seed motion behavior. Through single-factor and orthogonal experiments, the significant effects of operational parameters on uniformity indicators were identified. Using Matlab-based image processing, seeds were binarized and boundaries were accurately extracted via watershed segmentation, which ensured reliable calculation of the coefficient of variation of the nearest neighbor distance. Results demonstrated that the dispersing vibrator voltage most significantly affected uniformity, while the conveyor speed was the dominant factor influencing coverage rate (S). The optimal parameter combination was determined as: dispersing vibrator voltage of 120 V, leveling vibrator voltage of 70 V, and conveyor speed of 15 r/ min. Validation tests showed that under these parameters, the seed layer uniformity reached 48. 12% and coverage rate S achieved 40. 93%, representing an effective balance between distribution uniformity and processing efficiency. The research successfully achieved high-efficiency monolayer uniform coating of alfalfa seeds, providing a critical theoretical basis for uniform distribution technology in the seed processing industry. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Monolayers

Controlled terms: Dispersions? - ?Efficiency? - ?Electric discharges? - ?Flow control? - ?High speed cameras? - ?Leveling (machinery)? - ?MATLAB? - ?Plasma diagnostics? - ?Plasma theory? - ?Structural optimization ? - ?Vibrating conveyors? - ?Vibration analysis? - ?Vibrators

Uncontrolled terms: Alfalfa seeds? - ?Distribution systems? - ?High speed imaging? - ?Layer distributions? - ?Multi-stages? - ?Plasma treatment? - ?Single layer? - ?Uniform distribution? - ?Uniform single-layer distribution? - ?Vibration conveyance

Classification code: 208 Coatings, Surfaces, Finishes, Films and Deposition? - ?214 Materials Science? - ?301.1 Fluid Flow? - ?301.2.1.1 Plasma Dynamics? - ?601.1 Mechanical Devices? - ?692.1 Conveyors? - ?701.1 Electricity: Basic Concepts and Phenomena? - ?731.3 Specific Variables Control? - ?742.2 Photographic and Video Equipment? - ?913.1 Production Engineering? - ?941.5 Mechanical Variables Measurements? - ?1106.5 Computer Applications? - ?1201.5 Computational Mathematics? - ?1201.7 Optimization Techniques? - ?1301.2.3 Plasma Physics

Numerical data indexing: Angular velocity 2.505E-01rad/s, Percentage 1.20E+01%, Percentage 9.30E+01%, Voltage 1.20E+02V, Voltage 7.00E+01V

DOI: 10.6041/j.issn.1000-1298.2025.11.017

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

58. Design and Experiment of Living Entity Detection and Alert System for Silage Harvester Based on YOLO – TI

Accession number: 20255019669477

Title of translation: 基于 YOLO – TI 的青贮收获机活体检测预警系统设计与试验

Authors: Hui, Yunting (1, 2); Lü, Yuchan (1, 2); You, Yong (1, 2); Li, Hongqian (3); Wang, Hengyuan (1, 2); Wang, Guanghui (1, 2); Wang, Decheng (1, 2); Wang, Haiyi (1, 2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Intelligent Grassland Equipmentand & Smart Grassland Center, China Agricultural University, Beijing; 100083, China; (3) Shandong Wuzheng Group Co., Ltd., Rizhao; 276800, China

Corresponding author: Wang, Decheng(wdc@cau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 54-62

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In silage maize fields, the complex environment often obstructs the driver’s view, making harvesting operations prone to causing casualties and severe economic losses. To address the frequent accidents involving silage harvesters injuring humans and animals, a lightweight thermal infrared image detection model, YOLO – TI, was developed, and a thermal infrared-based living-body detection and early-warning system suitable for silage harvesters was designed. Thermal infrared images of living bodies under different times, locations, and postures were collected and preprocessed to optimize the YOLO v5 model. The backbone network of YOLO v5 was replaced with SqueezeNet for feature extraction, while an infrared spatial attention mechanism (ISAM) incorporated to enhance target features. Additionally, the CIoU loss function was replaced with SIoU to construct the YOLO – TI model. Experimental results showed that YOLO – TI achieved detection precision of 99. 6%, recall of 99. 1%, F1 score of 99. 0%, and real-time detection speed of 48 f/s. On the same test dataset, compared with YOLO v5, the proposed model improved precision, recall, F1 score, and detection speed by 4. 4, 2. 1, 3. 3 percentage points, and 11 f/s, respectively; compared with YOLO v10, precision and recall were improved by 0. 1, 1. 4 percentage points, respectively. Field test results indicated that at ambient temperature of 18℃, the system achieved average detection accuracy of 91. 5% within operating range of 0 ~ 15 m. Within the harvester’s minimum safety range of 2. 32 m, the response time from target appearance to alarm activation was 0. 45 s, meeting the safety requirements of harvesting operations. The system satisfied the demand for real-time detection of living bodies during operation, ensuring safety and reliability, and provided technical support for accurate and efficient living-body detection in complex agricultural scenarios with silage harvesters. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Computer vision

Controlled terms: Alarm systems? - ?Grain (agricultural product)? - ?Harvesters? - ?Infrared imaging? - ?Losses? - ?Signal detection? - ?Statistical tests

Uncontrolled terms: Entity detection? - ?F1 scores? - ?Harvesting operations? - ?Living bodies? - ?Living entity detection? - ?Machine-vision? - ?Silage harvesters? - ?Thermal infrared images? - ?Thermal-infrared? - ?YOLO – TI

Classification code: 716.1 Information Theory and Signal Processing? - ?741.1 Light/Optics? - ?746 Imaging Techniques? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?911.2 Industrial Economics? - ?914.1 Accidents and Accident Prevention? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 0.00E00%, Percentage 1.00E00%, Percentage 5.00E+00%, Percentage 6.00E+00%, Size 0.00E00m to 1.50E+01m, Size 3.20E+01m, Time 4.50E+01s

DOI: 10.6041/j.issn.1000-1298.2025.11.004

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

59. Design and Test of Light-weight Rice Straw Picking and Baling Integrated Machine

Accession number: 20254919640174

Title of translation: 轻简型水稻秸秆捡拾打捆一体机设计与试验

Authors: Jia, Pengcheng (1); Xu, Fudong (1); Wang, Jinwu (1); Deng, Yu (2, 3); Li, Jingyan (2); Tang, Han (1)

Author affiliation: (1) College of Engineering, Northeast Agricultural University, Harbin; 150030, China; (2) Heilongjiang Dewo Technology Development Co., Ltd., Harbin; 150086, China; (3) Heilongjiang Academy of Agricultural Machinery Engineering, Harbin; 150081, China

Corresponding author: Tang, Han(tanghan@neau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 298-307

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the current rice straw picking and baling operation in the northeastern region, there are problems such as difficulty in picking up, low operating efficiency, poor quality of bales and high labor cost, the 9Y 2230 light-weight straw picking and baling integrated machine was developed, which integrated the functions of picking up, conveying, compressing and tying in one machine, and achieved the function of non-falling baling of rice straw while guaranteeing the normal efficiency of the whole machine, which can complete the straw picking and baling operation at one time. Focusing on the optimized design and dynamic analysis of the key components of the screw feeding mechanism, toggle mechanism, baling piston mechanism, etc., the structure and operating parameters of the key components of the 9Y 2230 light-weight straw picking and baling integrated machine were determined. A single-factor test was used to determine the range of values for the forward speed of the machine, the feeding volume and the compression frequency; a three-factor, three-level orthogonal test was designed with the bale-forming rate and the regular bale rate as the evaluation indexes of the test, and with the forward speed of the machine, the feeding volume and the compression frequency as the test factors. The results of the orthogonal test were analyzed in terms of extreme variance, and the results showed that when the forward speed of the machine was 5. 00 km / h, the feeding volume was 4. 50 kg / s, and the compression frequency was 95. 00 r / min, the rate of bale formation at this time was up to 98. 30%, and the rate of regular bale was up to 95. 90% . This combination of parameters was verified in the field test, and the bale rate was 98. 29% and the regular bale rate was 95. 25%, and the relative errors with the optimized values were all less than 5%, and the research results can provide a reference for the design of the integrated straw picking and baling machine. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Wages

Controlled terms: Agricultural machinery? - ?Cost accounting? - ?Efficiency? - ?Feeding? - ?Machine components? - ?Orthogonal functions

Uncontrolled terms: Design optimization? - ?Field test? - ?Forward speed? - ?Integrated machines? - ?Kinetic analysis? - ?Light weight? - ?Orthogonal test? - ?Picking up? - ?Rice straws? - ?Straw baler

Classification code: 601.2 Machine Components? - ?691.2 Materials Handling Methods? - ?821.2 Agricultural Machinery and Equipment? - ?911.1 Cost Accounting? - ?912.3 Personnel? - ?913.1 Production Engineering? - ?1201 Mathematics

Numerical data indexing: Angular velocity 0.00E00rad/s, Mass flow rate 5.00E+01kg/s, Percentage 2.50E+01%, Percentage 2.90E+01%, Percentage 3.00E+01%, Percentage 5.00E+00%, Percentage 9.00E+01%, Size 0.00E00m

DOI: 10.6041/j.issn.1000-1298.2025.11.028

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

60. Multi-task Learning-based Method for Simultaneous Dairy Cow Body Condition Scoring and Individual Identification

Accession number: 20255019669013

Title of translation: 基于多任务学习的同步奶牛体况评分和个体识别方法

Authors: Jiang, Honghua (1, 2); Li, Jinhong (1, 2); Fa, Runxuan (3); Zhang, Guiguo (3); Mao, Wenhua (4); Qiao, Yongliang (5)

Author affiliation: (1) School of Information Science and Engineering, Shandong Agricultural University, Taian; 271018, China; (2) Smart Agriculture Characteristic Laboratory of Colleges and Universities in Shandong Province, Taian; 271018, China; (3) College of Animal Science and Technology, Shandong Agricultural University, Taian; 271018, China; (4) Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing; 100083, China; (5) Institute for Machine Learning, The University of Adelaide, Adelaide; SA5000, Australia

Corresponding author: Qiao, Yongliang(yongliang.qiao@ieee.org)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 581-589

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Precision breeding in animal husbandry is a crucial requirement of modern husbandry. However, there are still some issues in the research of cow body condition scoring and individual identification methods based on traditional machine vision in the actual feeding environment. These issues included low robustness, poor reliability, weak generalization, and timely correlation between multitasks. To address these challenges, a multi-task learning model was proposed based on the biological visual features of the back of cows to implement and optimize the body condition scoring and individual identification model of cows. Firstly, a depth camera was used to obtain the back image of the cow. Secondly, improvements were made to the YOLO v8s model to achieve automatic positioning and detection of the back area of cows. The GSConv module was introduced into the YOLO v8s network to reduce network parameters and simplify its structure. Additionally, weighted intersection over union loss was utilized as bounding box loss to enhance recognition accuracy. Based on detecting the back region of cows, further extraction was conducted on their biological visual features from this region. Finally, a multi-task learning model based on hard parameter sharing was constructed to simultaneously realize individual identification and body condition score evaluation of cows, leveraging the common features between cow body condition scoring and individual recognition tasks. The experimental results demonstrated that the proposed multi-task learning model achieved an accuracy of 93. 9% for body condition scoring and 92. 2% for individual identification, which was 1. 9 and 4. 5 percentage points higher than that of the single task model, respectively. Furthermore, the recognition time was reduced to 5. 84 ms, which was 6. 37 ms less than the time required by the single task model to complete two tasks at once, resulting in significantly improved real-time performance. In conclusion, a multi-task learning model was presented based on the biological visual features of the back region of cows, enabling efficient and accurate output of body condition score and individual identification simultaneously. The research result can provide a theoretical basis and technical support for precise individual management in large-scale intelligent breeding of dairy cows. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Multi-task learning

Controlled terms: Agricultural machinery? - ?Computer vision? - ?Dairies? - ?Learning systems? - ?Livestock

Uncontrolled terms: Biological visual information? - ?Body condition? - ?Body condition score? - ?Dairy cow? - ?Dairy cow detection? - ?Individual identification? - ?Learning models? - ?Multitask learning? - ?RGB – D? - ?Visual information

Classification code: 821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?822.1 Food Products Plants and Equipment? - ?1101.2 Machine Learning? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 2.00E+00%, Percentage 9.00E+00%, Time 3.70E-02s, Time 8.40E-02s

DOI: 10.6041/j.issn.1000-1298.2025.11.056

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

61. Optimal Design and Experiment of Furrow-cutting and Planting Mechanism for Rapeseed Blanket Seedling Transplanter

Accession number: 20254919668084

Title of translation: 油菜毯状苗移栽机切缝 -栽插装置优化设计与试验

Authors: Jiang, Lan (1); Tang, Qing (1); Wu, Chongyou (1); Wu, Jun (1); Zhu, Tingwei (1); Wang, Jixuan (1)

Author affiliation: (1) Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing; 210014, China

Corresponding author: Wu, Chongyou(542681935@qq.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 212-222

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of seedling lodging, unstable planting depth, and inconsistent planting quality during high-speed operations of rapeseed blanket seedling transplanter, a novel flat-bed furrow-cutting and aligned planting method was proposed. The primary shape parameters of the planting furrow were determined based on the planting needle and seedling block parameters. Mechanical resistance test during seedling block insertion into furrow obtained the dynamic variations of soil resistance acting on the seedling block. Dynamic analysis of the seedling-pushing process into furrow revealed the interaction mechanisms between the planting mechanism, soil, and seedlings during transplantation. Ultimately, the critical parameter ranges affecting planting quality were determined. Utilizing a soil bin test-bed, a quadratic orthogonal combination experiment was designed with three critical factors: furrow cone angle (X1 ), seedling-pushing spring stiffness coefficient (X2 ), and planting frequency (X3 ) . Planting depth qualification rate and seedling uprightness rate were selected as evaluation indices. Experimental results demonstrated that the optimal performance was obtained when X1 = 70°, X2 = 2. 32 N / mm, and X3 = 280 hills / min, achieving 99. 56% planting depth qualification rate and 91. 55% seedling uprightness rate. To validate the operational performance of the furrow-cutting and planting mechanism, the field validation tests were conducted with an improved prototype. The results showed that the planting depth qualification rate was 97. 76%, the seedling uprightness rate was 92. 22%, the planting qualification rate was 91. 84% . The planting quality was significantly improved before optimization. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Oilseeds

Controlled terms: Dynamic mechanical analysis? - ?Dynamics? - ?Germination? - ?Plants (botany)? - ?Soil testing? - ?Soils? - ?Stiffness

Uncontrolled terms: Cutting mechanisms? - ?Furrow-cutting mechanism? - ?Optimal experiments? - ?Planting depth? - ?Planting mechanism? - ?Plantings? - ?Rapeseed blanket seedling? - ?Seedling uprightness? - ?Transplanter

Classification code: 103 Biology? - ?214 Materials Science? - ?215.1 Testing of Mechanical Properties of Materials? - ?483.1 Soils and Soil Mechanics? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?1301.1.1 Mechanics? - ?1301.7 Statistical and Nonlinear Physics? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Percentage 2.20E+01%, Percentage 5.50E+01%, Percentage 5.60E+01%, Percentage 7.60E+01%, Percentage 8.40E+01%, Surface tension 3.20E+04N/m

DOI: 10.6041/j.issn.1000-1298.2025.11.020

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

62. Semi-supervised Object Detection Method for Spots of Tea Leaf Blight Based on UAV Remote Sensing Images

Accession number: 20254919668131

Title of translation: 基于无人机遥感图像的茶叶枯病病斑半监督检测方法

Authors: Jiang, Yongcheng (1); Lü, Yunxian (1); Hu, Gensheng (2)

Author affiliation: (1) School of Electrical Engineering and Automation, Anhui University, Hefei; 230601, China; (2) National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei; 230601, China

Corresponding author: Hu, Gensheng(hugs2906@sina.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 441-452

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate detection of tea leaf blight (TLB) from low-resolution images of tea trees is an important and challenging task. Due to the dense blight spots, small areas, and blurred edges of TLB in unmanned aerial vehicle (UAV) remote sensing images, existing methods exhibited low detection accuracy. Moreover, current TLB detection methods typically relied on fully supervised learning, which required a large amount of expert annotation, making it time-consuming and labor-intensive. To address this, a semi-supervised target detection method of semi-supervised object detection method for spots of tea leaf blight in UAV remote sensing images (SSTLBdet) was proposed for detecting small, dense, and blurred TLB spots in UAV remote sensing images. This method utilized limited labeled information through iterative annotation to screen out more targets with high uncertainty, rich visual information, and diverse size distributions for detection, additionally, it introduced weighted joint confidence estimation (WJCE) and adaptive sample selection (ASS), effectively enhancing the ability to handle dense, ambiguous, and small lesions. Experimental results showed that SSTLBdet achieved mAP values of 51. 28% (1% labeled data),59. 85% (5% labeled data), 75. 09% (10% labeled data), 77. 99% (20% labeled data), and 78. 12% (30% labeled data). Compared with the FCOS model, SSTLBdet improved the mAP value on the test data by 32. 70 percentage points. In terms of mAP@ 0. 5 and F1 score, SSTLBdet achieved 78. 12% and 71. 24%, respectively, significantly outperforming other detection methods. The proposed method significantly enhanced the detection accuracy of tea leaf blight spots while significantly reducing the workload of data annotation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Main heading: Object detection

Controlled terms: Aircraft detection? - ?Antennas? - ?Deep learning? - ?Iterative methods? - ?Labeled data? - ?Learning systems? - ?Object recognition? - ?Remote sensing? - ?Semi-supervised learning? - ?Tea ? - ?Unmanned aerial vehicles (UAV)? - ?Vehicle detection

Uncontrolled terms: Aerial vehicle? - ?Deep learning? - ?Labeled data? - ?Leaf blights? - ?Objects detection? - ?Remote sensing images? - ?Semi-supervised? - ?Tea leaf blight? - ?Tea-leaves? - ?Unmanned aerial vehicle remote

Classification code: 435.2 Tracking and Positioning? - ?652.1 Aircraft? - ?716.2 Radar Systems and Equipment? - ?716.5.1 Antennas? - ?731.1 Control Systems? - ?822.3 Food Products? - ?903.1 Information Sources and Analysis? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1201.9 Numerical Methods

Numerical data indexing: Percentage 1.00E+01%, Percentage 1.00E00%, Percentage 1.20E+01%, Percentage 2.00E+01%, Percentage 2.40E+01%, Percentage 2.80E+01%, Percentage 3.00E+01%, Percentage 5.00E+00%, Percentage 8.50E+01%, Percentage 9.00E+00%, Percentage 9.90E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.042

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

63. Named Entity Recognition Method for Tea Tree Pests and Diseases Based on BERT BiLSTM CRF

Accession number: 20254919628193

Title of translation: 基于 BERT BiLSTM CRF 的茶树病虫害命名实体识别方法

Authors: Li, Chunchun (1, 2); Ding, Xin (1); Zhang, Huayang (1); Wang, Dejiong (1); Wang, Ziyang (1); Jiang, Zixi (1); Lei, Yu (1, 2)

Author affiliation: (1) School of Internet, Anhui University, Hefei; 230601, China; (2) National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei; 230601, China

Corresponding author: Lei, Yu(leiyu@ahu.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 517-527

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Rapidly and accurately extracting disease entities from unstructured tea tree pest and disease text data is crucial for the efficient management and prevention of tea tree pests and diseases. Currently, there is a lack of publicly available, high-quality datasets in the field of tea plant pests and diseases. Moreover, traditional named entity recognition (NER) methods often suffer from issues such as ambiguous entity boundaries and low recognition accuracy. To address these challenges, the annotated corpus specifically for tea plant pests and diseases was constructed and a NER method for tea tree pests and diseases was proposed based on the BERT BiLSTM CRF architecture. Firstly, the BERT model was employed to obtain context-related word vector representations to improve the semantic understanding ability of tea tree pests and diseases; the BiLSTM module was used to fuse forward and reverse information to capture long-distance dependencies and deeply explore the deep semantic features of tea tree pests and diseases; the CRF layer was used to globally optimize the label sequence to improve the entity boundary recognition accuracy of tea tree pests and diseases. Experimental results showed that the proposed model achieved a precision of 98. 42%, a recall of 98. 75%, and an F1-score of 98. 58% on the self-constructed dataset, reaching state-of-the-art performance. Additionally, F1-scores of 92. 4% and 87. 8% were achieved on the general-domain datasets MSRA NER and CLUENER2020, respectively, demonstrating the model’s generalization capability. Research findings not only provided a paradigm reference for knowledge extraction in the agricultural field by integrating linguistic rules and deep learning, but also provided technical support for building a knowledge graph and question-answering system for tea pests and diseases, thereby helping tea growers and agricultural technicians to make efficient decisions. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 40

Main heading: Semantics

Controlled terms: Data mining? - ?Deep learning? - ?Forestry? - ?Information management? - ?Knowledge graph? - ?Learning systems? - ?Natural language processing systems? - ?Plant diseases? - ?Tea

Uncontrolled terms: BERT? - ?Knowledge graphs? - ?Named entity recognition? - ?Plant disease? - ?Plant pests? - ?Question answering systems? - ?Recognition accuracy? - ?Recognition methods? - ?Tea plants? - ?Tea tree pest and disease

Classification code: 103 Biology? - ?821.1 Woodlands and Forestry? - ?822.3 Food Products? - ?903 Information Science? - ?903.2 Information Dissemination? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1106.2 Data Handling and Data Processing? - ?1106.2.1 Data Mining? - ?1106.7 Computational Linguistics

Numerical data indexing: Percentage 4.00E+00%, Percentage 4.20E+01%, Percentage 5.80E+01%, Percentage 7.50E+01%, Percentage 8.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.11.050

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

64. Cam-driven Seedling Picking and Placing Transmission Mechanism of Vegetable Transplanters Based on Reverse Motion Law

Accession number: 20255019669053

Title of translation: 基于反转运动规律的蔬菜移栽机凸轮取投苗传动机构研究

Authors: Li, Hongshuang (1); Ma, Jianfeng (1)

Author affiliation: (1) Mechanical and Electronic Engineering Institute, Shenyang Aerospace University, Shenyang; 110136, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 223-233 and 263

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to further enhance the stability and efficiency of the vegetable transplanting machine’s seedling picking and placing process, a double cam and link-type seedling picking and placing transmission mechanism was designed for the clamping type seedling picking and placing method. Based on the displacement requirements, the structure was designed. By using the reverse motion law method, the motion law of the input end rocker was determined by reversing the ideal motion law of the output end, and then the cam profile corresponding to the high-demand and large-stroke horizontal seedling feeding section was determined. The motion process of the mechanism was simulated and analyzed by ADAMS, and the results were compared with the theoretical results to verify the correctness and feasibility of the design. A seedling picking and placing test bench was built, and pepper pot seedlings were selected as the test objects. The seedling age, picking speed and substrate moisture content were selected as the test factors, and single-factor tests were designed with the picking success rate, placing success rate and pot body damage rate as the evaluation indicators. The Box – Behnken response surface analysis method was used to design orthogonal tests, the interaction effects of the test factors on the seedling picking and placing effect were obtained, and the optimized picking parameters were obtained. Verification tests were carried out, and the experimental results showed that when the seedling age was 50 days, the picking speed was 56 plants/min, and the substrate moisture content was 52%, the picking success rate was 97. 58%, the placing success rate was 95. 64%, and the pot body damage rate was 5. 32%, which met the technical requirements of vegetable transplanting operations. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 20

Main heading: Cams

Controlled terms: Agricultural machinery? - ?Clamping devices? - ?Moisture? - ?Plants (botany)? - ?Seed? - ?Surface analysis? - ?Surface testing? - ?Vegetables

Uncontrolled terms: Cam linkage? - ?Clamping-type? - ?Feeding devices? - ?Motion law? - ?Reverse motion law? - ?Seedling age? - ?Seedling feeding device? - ?Substrate moisture contents? - ?Transmission mechanisms? - ?Vegetable transplanter

Classification code: 103 Biology? - ?208 Coatings, Surfaces, Finishes, Films and Deposition? - ?215 Materials Testing? - ?601.2 Machine Components? - ?601.3 Mechanisms? - ?605.2 Small Tools, Unpowered? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products

Numerical data indexing: Age 1.37E-01yr, Percentage 3.20E+01%, Percentage 5.20E+01%, Percentage 5.80E+01%, Percentage 6.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.021

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

65. Adaptive Regulation of Low-loss Threshing in Wheat Based on BP–NSGA – Ⅱ Algorithm

Accession number: 20254919668196

Title of translation: 基于 BP–NSGA– Ⅱ 算法的小麦低损脱粒自适应调控方法

Authors: Li, Hua (1); Lü, Dawei (1); Wang, Yongjian (1); Jin, Chengqian (2); Wang, Lihui (3); Yao, Cong (1)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 210000, China; (2) Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing; 210000, China; (3) College of Instrument Science and Engineering, Southeast University, Nanjing; 210000, China

Corresponding author: Li, Hua(lihua@njau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 243-252

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The traditional harvester could not adjust the operating parameters in real time according to the field environment and operating speed, resulting in high entrainment loss rate and impurity rate of excavated products, and low operating performance. A low-loss threshing adaptive control method for wheat based on machine learning algorithm was proposed, which realized low-loss threshing by monitoring wheat feed and crop characteristics in real time, dynamically adjusting the drum speed and threshing gap to achieve low-loss threshing. Based on discrete element simulation and BP neural network, the threshing performance indexes under different feeding rates and wheat moisture content were predicted. The trained BP neural network model was used as the fitness function of the NSGA – Ⅱ algorithm to optimize the drum speed and threshing gap, and the drum speed and threshing gap control model based on the BP – NSGA – Ⅱ algorithm was established, and the adaptive adjustment of drum speed and threshing gap was realized. The threshing performance of the control system was compared with the performance of the constant parameter threshing device, and the superiority of the control model was verified. The field experiment results showed that the performance of the threshing device with control system was significantly better than that of the constant parameter threshing device when the wheat harvest was carried out under different feeding conditions, and the entrainment loss rate and the impurity rate of the escaped were reduced by 11%, 14%, 12% and 8%, 12% and 9%, respectively, when the advance speed was 0. 8 m / s, 1. 2 m / s and 1. 6 m / s. The results can provide a reference for the research on low-loss threshing technology of wheat and other crops in complex and changeable field environments. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Crops

Controlled terms: Adaptive control systems? - ?Harvesters? - ?Learning algorithms? - ?Learning systems? - ?Machine learning? - ?Neural networks

Uncontrolled terms: Adaptive regulation? - ?BP-NSGA ⅱ algorithm? - ?Drum speed? - ?Entrainment loss? - ?Impurity rates? - ?Loss rates? - ?Low-loss? - ?Performance? - ?Real- time? - ?Wheat harvester

Classification code: 101.1 Biomedical Engineering? - ?731.1 Control Systems? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning

Numerical data indexing: Percentage 1.10E+01%, Percentage 1.20E+01%, Percentage 1.40E+01%, Percentage 8.00E+00%, Percentage 9.00E+00%, Velocity 2.00E+00m/s, Velocity 6.00E+00m/s, Velocity 8.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2025.11.023

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

66. Positioning, Orientation and Pre-cutting Attitude Adjustment of Seeds for Wheat Slicer

Accession number: 20254919668185

Title of translation: 小麦切片机种子定位定向与预切姿态调整系统研究

Authors: Li, Yang (1); Chen, Yunfei (1); Guo, Xiangyu (2, 3); Li, Tianhua (1, 4); Wang, Shining (1); Guo, Jing (1, 5)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China; (2) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou; 310058, China; (3) Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou; 310058, China; (4) Shandong Engineering Research Center of Agricultural Equipment Intelligentization, Taian; 271018, China; (5) Shandong Key Laboratory of Intelligent Production Technology and Equipment for Facility Horticulture, Taian; 271018, China

Corresponding author: Li, Tianhua(lth5460@163.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 329-338

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the process of positioning orientation and attitude adjustment of seeds for developed wheat slicers, there are problems of low recognition and positioning accuracy, and difficult to distinguish the attitude of seeds because of the small particle size and irregular shape of seeds. On the basis of the mechanical structure of the wheat seed slicer designed in the early stage, a set of seed positioning orientation and pre-cutting attitude adjustment system were developed to provide a good pre-cutting posture for wheat seed slices. Firstly, the structure of the seed positioning and attitude adjustment system, including industrial camera, parallel mechanical arm and electric grippers was designed. And the hardware parameters were selected to deploy the system reasonably and clarify its working principle. Secondly, an industrial camera was used to collect wheat seed images in order to establish a wheat seed dataset and semantically annotate the germ of wheat. A wheat seed recognition segmentation network was trained based on the semantic segmentation framework of YOLO v8, and the model was structurally improved by incorporating the Shuffle Attention mechanism in order to increase the segmentation accuracy of the seed images. And on the basis of accurate semantic segmentation of seeds, an algorithm was proposed to find the shape center from the mask contour by matching the shape center of the endosperm and germ end of the seed, in order to realize the efficient and accurate detection of the position and attitude of wheat seeds. Finally, the experimental results showed that the weight parameter obtained by adding the attention mechanism was 1. 50% higher than that of the original model, the average precision mean value was increased by 8. 46%, the average detection time of the improved YOLO v8 model on the dataset was 18. 91 ms, the correctness rate of wheat seed pose adjustment was 91. 30%, the average correctness rate of the pose detection algorithm was 92. 93%, and the error range of the pose angle was - 3. 50° ~ 3. 50°, the average absolute error obtained was 1. 66°, the average time consumed in the seed data acquisition stage was 9. 17 s, the average time consumed in the grasping stage was 6. 21 s, the average time consumed in the returning to the cutting area stage was 5. 74 s, and the average operating time of the whole workflow was 21. 11 s, which was within the allowable error range, which verified that the research results in the positioning of wheat seeds and attitude recognition had a high accuracy, and can effectively carry out the adjustment of seed pre-cutting attitude. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Semantics

Controlled terms: Cameras? - ?Data acquisition? - ?Errors? - ?Gesture recognition? - ?Plants (botany)? - ?Seed? - ?Semantic Segmentation

Uncontrolled terms: Attention mechanisms? - ?Attitude adjustment? - ?Centroid matching? - ?Correctness rates? - ?Matchings? - ?Seed positioning and orientation? - ?Semantic segmentation? - ?Slicer? - ?Wheat? - ?Wheat seeds

Classification code: 103 Biology? - ?731.1.1 Error Handling? - ?742.2 Photographic and Video Equipment? - ?821.5 Agricultural Products? - ?903.2 Information Dissemination? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 3.00E+01%, Percentage 4.60E+01%, Percentage 5.00E+01%, Percentage 9.30E+01%, Time 1.10E+01s, Time 1.70E+01s, Time 2.10E+01s, Time 7.40E+01s, Time 9.10E-02s

DOI: 10.6041/j.issn.1000-1298.2025.11.031

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

67. Design and Experiment of Variable-diameter and Variable-pitch Spiral Mixing and Seeding Device

Accession number: 20255019701316

Title of translation: 变径变距式螺旋混合排种装置设计与试验

Authors: You, Yong (1); Zhou, Xiaoyi (1); Guo, Xiaoqing (2); Ji, Kun (1); Xi, Junhui (1); Guo, Yanying (1); Hui, Yunting (1)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Inner Mongolia Autonomous Region Agriculture and Animal Husbandry Technology Promotion Center, Hohhot; 010010, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 93-102

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The intercropping of alfalfa and smooth brome can improve soil structure, enhance forage quality and strengthen ecosystem stability. To address the problems of seed blockage and uneven mixing caused by differences in seed shape, size and fluidity between the two, a variable-diameter and variable-pitch spiral mixing and seeding device was designed to improve the intercropping quality of alfalfa and smooth brome. Through mechanical analysis and parameter calculations, the conveying and mixing mechanisms of the device were clarified and key structural parameters such as the spiral diameter, pitch and shaft length were determined. Using discrete element method (DEM) simulations, the effects of the spiral blade angle on seeding performance and mixing effectiveness were analyzed to obtain the optimal structural parameters. With the rotational speed of the spiral shaft and the filling rate of the mixed seeds as factors, a two-factor, five-level rotational combination bench test was conducted. Seeding rate, coefficient of variation of seeding stability and mixing ratio accuracy were used as evaluation indicators, and the response surface method was employed to optimize the combination of working parameters. The results showed that when the spiral blade angle was 25°, the seeding performance was optimal, with a seeding rate of 3.34 g/s, a coefficient of variation of 5.17% and a mixing ratio accuracy of 0.874. The bench test determined that the optimal working parameter combination of a spiral shaft speed of 45 r/min and a filling rate of mixed seeds of 70% resulted in a seeding rate of 3.49 g/s, a coefficient of variation of 4.13% and a mixing ratio accuracy of 0.946, with the validation test showing a relative error of less than 10%. The device had a rational structure, which can achieve stable and uniform mixing and seeding of the two types of seeds, meeting the requirements for intercropping alfalfa and smooth bromegrass and provided a technical basis for the development of intercropping equipment for forage crops. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Mixing

Controlled terms: Agricultural machinery? - ?Crops? - ?Discrete element methods? - ?Ecosystems? - ?Finite difference method? - ?Plants (botany)? - ?Soil testing? - ?Structural optimization

Uncontrolled terms: Coefficients of variations? - ?Mixing ratios? - ?Peer mixed sowing? - ?Seeding performance? - ?Seeding rate? - ?Spiral mixing and seeding device? - ?Structural parameter? - ?Variable diameter? - ?Variable pitch? - ?Variable-diameter and variable-pitch

Classification code: 103 Biology? - ?483.1 Soils and Soil Mechanics? - ?802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?1201.5 Computational Mathematics? - ?1201.7 Optimization Techniques? - ?1201.9 Numerical Methods? - ?1502.1.1.4.3 Soil Pollution Control? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Angular velocity 7.515E-01rad/s, Mass flow rate 3.34E-03kg/s, Mass flow rate 3.49E-03kg/s, Percentage 1.00E+01%, Percentage 4.13E+00%, Percentage 5.17E+00%, Percentage 7.00E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.008

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

68. Review of Research on Green Forage Harvesting Technologies and Equipment

Accession number: 20255119718982

Title of translation: 青饲料收获技术与装备研究综述

Authors: You, Yong (1); Hu, Pengzhan (1); Wang, Decheng (1); Hui, Yunting (1); Zhang, Xiaohang (1); Xu, Yifan (1); Ji, Zhongliang (2); Teng, Shaomin (3)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Shandong Wuzheng Takakita Stockbreeding Machinery Co., Ltd., Rizhao; 262306, China; (3) Menoble Co., Ltd., Beijing; 100083, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 21-41

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: As a high-quality feed source for animal husbandry, the mechanized harvesting and production technology of green forage is an important support for ensuring the efficient development of the forage industry. Among them, green forage harvesting equipment is a key device for supplying high-quality forage to the animal husbandry sector. To systematically grasp the technological trends in this field, this paper started with the composition structure and operating principle of green forage harvesters, focuses on sorting out the research progress in core links such as forage cutting and conveying, feeding and chopping, grain crushing, throwing and filling, and intelligent sensing and control of the whole machine, and analyzed and discusseed the key focuses, difficulties, and existing shortcomings in the relevant research fields of green forage harvesters at home and abroad. At present, China has established a relatively complete design theory system for green forage harvesters and possesses independent R&D and production capabilities. However, there are still problems such as the lag in basic theoretical research, the low degree of independence in core technologies, and the relatively low level of intelligentization of the whole machine. In the future, research should be carried out from three dimensions: breakthroughs in basic mechanisms, innovation in key technologies, and construction of intelligent systems. This is to promote the upgrading of green forage harvesters from “mid-to-low-end adaptation” to “high-end independent and controllable”, and provide equipment support for the high-quality development of China’s forage industry. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 129

Main heading: Reviews

Controlled terms: Agriculture? - ?Animals? - ?Construction equipment? - ?Cutting equipment? - ?Engineering research? - ?Harvesters? - ?Intelligent systems? - ?Plants (botany)

Uncontrolled terms: Animal husbandry? - ?Crop adaptability? - ?Green forage harvester? - ?High quality? - ?Intelligence? - ?Mechanized harvesting? - ?Production technology? - ?Technological trends? - ?Technology and equipments? - ?Whole machine

Classification code: 103 Biology? - ?405.1 Construction Equipment? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?901.3 Engineering Research? - ?903.2 Information Dissemination? - ?942.2 Miscellaneous Devices, Equipment and Components? - ?1101 Artificial Intelligence

DOI: 10.6041/j.issn.1000-1298.2025.11.002

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

69. Design and Experiment of Bionic Subsoiler for Leymus chinensis Grasslands Based on Claw-toe Curve of Myospalax aspalax

Accession number: 20255019701318

Title of translation: 基于草原鼢鼠爪趾曲线的羊草草地松土铲设计与试验

Authors: He, Changbin (1, 2); Yao, Yi (1); Guo, Yanying (1, 3); Yuan, Shuai (4); Zhou, Jiancheng (5); Bao, Pengyu (1)

Author affiliation: (1) College of Mechaical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China; (2) Inner Mongolia Engineering Research Center of Intelligent Equipment for the Entire Process of Forage and Feed Production, Hohhot; 010018, China; (3) College of Engineering, China Agricultural University, Beijing; 100083, China; (4) College of Grassland Science, Inner Mongolia Agricultural University, Hohhot; 010018, China; (5) Inner Mongolia Hongchang Machinery Manufacturing Co., Ltd., Hohhot; 010200, China

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 103-112

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of high operational resistance and poor loosening effectiveness of soil loosening components during the cultivation of Leymus chinensis grasslands, a claw-toe-shaped integrated subsoiler and a biomimetic wing-shaped subsoiler were designed based on the inner curve profile of the middle toe of Myospalax aspalax. Field test was conducted to validate the operational performance. Building on this, a biomimetic wing-shaped subsoiler was developed to enhance the loosening effect, and an optimization experiment on the structural parameters of the shovel wings was carried out, the installation height of shovel wings, opening angle of wings, and tilt angle of wings were selected as experimental factors, the evaluation indicators included soil disturbance area, turnover rate, and cultivation resistance. The experimental results indicated that the biomimetic subsoiler significantly reduced the resistance during the grassland loosening operation, and the biomimetic wing-shaped shovel demonstrated a good soil loosening effect. The installation height and tilt angle of the wings had a substantial impact on the overall operational resistance and soil disturbance. The optimal structural parameter combination was found to be: wing opening angle of 105°, wing tilt angle of 20°, and wing installing height of 5 cm, the biomimetic wing-shaped shovel operation resistance was 5 501.38 N, soil disturbance factor was 68.7%, over-turning rate was 9.7%, compared with before optimization, the soil disturbance factor was increased by 31.6%. These findings provided technical supports and data for the development of specialized soil loosening components for degraded grassland improvement. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Shovels

Controlled terms: Agricultural machinery? - ?Biomimetic processes? - ?Cultivation? - ?Curve fitting? - ?Plants (botany)? - ?Soil testing? - ?Soils? - ?Structural optimization

Uncontrolled terms: Curve of myospalax claw-toe? - ?Grassland improvement? - ?Leymus chinensis? - ?Optimisations? - ?Soil disturbances? - ?Soil loosening? - ?Structural parameter? - ?Subsoiler? - ?Tillage resistence? - ?Tilt angle

Classification code: 101.7 Biotechnology? - ?103 Biology? - ?483.1 Soils and Soil Mechanics? - ?605.2 Small Tools, Unpowered? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?1201.7 Optimization Techniques? - ?1201.9 Numerical Methods? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Percentage 6.87E+01%, Percentage 9.70E+00%, Size 5.00E-02m, Force 5.0138E+02N, Percentage 3.16E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.009

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

70. Estimation Model of Grassland above Ground Biomass Integrating Three-dimensional Structure and Spectral Characteristics of Vegetation

Accession number: 20255119731854

Title of translation: 融合植被三维结构与光谱特征的草原地上生物量估算模型

Authors: Wang, Tianyi (1, 2); Lu, Mengyuan (1); He, Gang (3); Wang, Qiang (4)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Smart Agricultural System Integration, Ministry of Education, China Agricultural University, Beijing; 100083, China; (3) Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing; 100083, China; (4) Chinese Academy of Agricultural Mechanization Sciences Co., Ltd., Hohhot Branch, Hohhot; 010010, China

Corresponding author: Wang, Qiang(wangqiangngd@126.com)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 76-83

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate estimation research of above ground biomass (AGB) in temperate typical grasslands was constrained by the limitations of traditional spectral remote sensing, which failed to capture three-dimensional (3D) structural information, while high-precision 3D data acquisition technologies such as LiDAR are often associated with high costs. To address these issues, a fusion method based on consumer-grade depth cameras and UAV multispectral technology was proposed, with the grassland in Chabei Management Zone, Zhangjiakou City, selected as the research area. Top-view images of all quadrats (0. 5 m × 0. 5 m each) were captured by using a ZED2i depth camera. Each quadrat was divided into 25 fine scanning kernels (0. 1 m × 0. 1 m), and the vegetation projected volume index (VPVI) was extracted to characterize the “height coverage” coupling feature of vegetation. The VPVI was calculated by summing the product of effective height (derived from depth data) and green vegetation coverage (obtained via HSV segmentation) across all kernels, thereby quantifying the 3D volume of vegetation in a simplified manner. Meanwhile, single-band reflectances (red, near-infrared) and multiple vegetation indices (VIs, including NDVI, PRI) were extracted from UAV multispectral images. Principal component analysis (PCA) was applied to filter these spectral data, and core features sensitive to AGB were retained. Three AGB inversion models were constructed by using the random forest algorithm: VPVI-only model, VIs-only model, and VPVI VIs fusion model. The results showed that the fusion model achieved the optimal performance. Its training set exhibited coefficient of determination (R2 ) of 0. 92 and root mean square error (RMSE) of 8. 49 g / m2 , while the test set yielded R2 of 0. 87 and RMSE of 13. 77 g / m2 . These results were significantly better than those of the VPVI-only model (test set R2 = 0. 73) and the VIs-only model (test set R2 = 0. 77) . Feature importance analysis indicated that VPVI accounted for 42% of the contribution in the fusion model, serving as the core feature, while the combined contribution of PRI and NDVI reached 24%, acting as key supplements. Scatter plot validation demonstrated that stable predictions were achieved in the AGB range of 30 ~ 150 g / m2 , with high coverage rate of the 95% prediction interval. The rsearch result confirmed that the fusion of VPVI and VIs effectively improved the accuracy of grassland AGB inversion, providing a low-cost and efficient technical support for large-scale grassland biomass monitoring. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Principal component analysis

Controlled terms: 3D modeling? - ?Biomass? - ?Cameras? - ?Data acquisition? - ?Image segmentation? - ?Integral equations? - ?Mean square error? - ?Reflection? - ?Remote sensing? - ?Statistical tests ? - ?Three dimensional computer graphics? - ?Unmanned aerial vehicles (UAV)? - ?Vegetation

Uncontrolled terms: Aboveground biomass? - ?Estimation models? - ?Fusion model? - ?Grass above ground biomass? - ?Multi-spectral? - ?Three dimensional structure of vegetation? - ?Three-dimensional structure? - ?UAV multispectral? - ?Vegetation projection volume index? - ?Volume index

Classification code: 103 Biology? - ?652.1 Aircraft? - ?731.1 Control Systems? - ?742.2 Photographic and Video Equipment? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?902.1 Engineering Graphics? - ?1008 Renewable Energy? - ?1008.7 Bioenergy and Biomass Energy Conversion? - ?1101.2 Machine Learning? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing? - ?1201.2 Calculus and Analysis? - ?1201.12 Modeling and Simulation? - ?1202.2 Mathematical Statistics? - ?1301.3 Optics

Numerical data indexing: Linear density 3.00E-02kg/m to 1.50E-01kg/m, Linear density 4.90E-02kg/m, Linear density 7.70E-02kg/m, Percentage 2.40E+01%, Percentage 4.20E+01%, Percentage 9.50E+01%, Size 1.00E00m, Size 5.00E+00m

DOI: 10.6041/j.issn.1000-1298.2025.11.006

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

71. Design and Experiment of Chain-tooth Type Chopped Forage Uniform Feeding Device for Round Baler

Accession number: 20260119859396

Title of translation: 圆捆机链齿式饲草碎料均匀供料装置设计与试验

Authors: Ma, Pengbo (1); Li, Wenxi (1); Guo, Lei (2); You, Yong (1); Yu, Chen (2); Li, Sibiao (1); Paheerding, Abulaiti (2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Agricultural Equipment Institute, Xinjiang Academy of Agricultural Sciences, Urumqi; 830002, China

Corresponding author: You, Yong(youyong@cau.edu.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: November 2025

Publication year: 2025

Pages: 113-121

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of uneven feeding of chopped forage, significant fluctuations in feed volume, and even blockages during round baler operations, a chain-tooth-type method for uniform feeding of chopped forage suitable for stationary round balers was proposed, along with the design of a uniform feeding device. The device primarily consisted of a conveyor chain plate, agitating chain teeth, a top plate, and a drive unit. It utilized the agitating action of multiple rows of chain teeth to achieve continuous, uniform feeding of chopped forage. Bench testing demonstrated that throughout the feeding process, the agitating chain plates effectively disrupted fiber entanglement and adhesion between upper-layer materials, improving material flow within the hopper. Lower-layer materials were continuously and stably fed by the conveyor chain plates, achieving uniform material discharge and validating the rationality of the chain-tooth feeding method. Building upon bench testing, a prototype feeding device was manufactured and installed for field trials. A two-factor, five-level central composite orthogonal test was conducted, with agitating chain plate speed and feed roller speed as experimental factors, and average oil pressure of the agitating chain plate, feeding time, and coefficient of variation in the lateral height of discharged material as evaluation metrics. This yielded the device’s optimal operating parameters. Results indicated that the optimal feeding performance was achieved with a combination of 13 r / min for the agitated chain plate and 85 r / min for the feed roller. At this configuration, the average oil pressure was 1. 48 MPa, the feeding time was 128 s, and the height coefficient of variation was 44. 81%, meeting the requirements for uniform feeding operations. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Feeding

Controlled terms: Chains? - ?Conveyors? - ?Materials testing? - ?Oil field equipment? - ?Rollers (machine components)

Uncontrolled terms: Bench testing? - ?Chopped forage? - ?Coefficients of variations? - ?Feed volume? - ?Feeding devices? - ?Feeding time? - ?Layer materials? - ?Oil pressures? - ?Round baler? - ?Tooth type

Classification code: 214 Materials Science? - ?511.2 Oil Field Equipment? - ?601.2 Machine Components? - ?602.1 Mechanical Drives? - ?691.2 Materials Handling Methods? - ?692.1 Conveyors

Numerical data indexing: Angular velocity 1.4195E+00rad/s, Angular velocity 2.171E-01rad/s, Percentage 8.10E+01%, Pressure 4.80E+07Pa, Time 1.28E+02s

DOI: 10.6041/j.issn.1000-1298.2025.11.010

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.

72. Hyperspectral Identification of Upright Maize Straw in Autumn and Winter

Accession number: 20254919629190

Title of translation: 秋冬季未离田直立玉米秸秆高光谱可识别性研究

Authors: Chao, Aosheng (1, 2); Zhu, Qingwei (1); Xing, Enguang (2, 3); Li, Cunjun (2, 4); Liu, Yu (2, 4); Zhang, Jinhao (2, 3)

Author affiliation: (1) College of Geomatics, Xi’an University of Science and Technology, Xi’an; 710054, China; (2) Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (3) School of Civil Engineering, University of Science and Technology Liaoning, Anshan; 114051, China; (4) Key Laboratory of Agricultural Remote Sensing Mechanism and Quantitative Remote Sensing, Ministry of Agriculture and Rural Affairs, Beijing; 100097, China

Corresponding author: Li, Cunjun(licj@nercita.org.cn)

Source title: Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 11

Issue date: 2025

Publication year: 2025

Pages: 387-396

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Investigating the spectral characteristics and sensitive bands of the upright maize straw during this period forms the basis for accurately identifying straw among similar land cover types. Hyperspectral data for the upright maize straw and nine other types of land cover were collected during the same period. The spectral curve features of raw spectra and three mathematical transformations were analyzed, and their separability was evaluated. Utilizing stepwise discriminant analysis (SDA) and competitive adaptive reweighted sampling (CARS), sensitive spectral bands for standing the upright maize straw were identified. Subsequently, four classifiers—random forest (RF), convolutional neural network (CNN), long short-term memory recurrent neural network (LSTM), and particle swarm optimization back propagation neural network (PSO_BP) —were employed for identifying the upright maize straw left in the field. The results indicated that the upright maize straw exhibited the most pronounced differences in the shortwave infrared region, followed by the visible light region. Among the four spectral datasets, raw spectra showed lower recognition effectiveness, while transformed spectra achieved high accuracy, with an average recognition precision exceeding 89%, particularly for the first-order differential transformed spectral data. Regarding feature wavelength selection methods, SDA-based approaches demonstrated the highest recognition accuracy. All four classification methods met the accuracy requirements for identification, with PSO_BP achieving the highest precision. These experiments demonstrated the upright maize straw during autumn and winter exhibited unique spectral characteristics and sensitive bands, providing theoretical support for remote sensing monitoring by using satellites and unmanned aerial vehicles (UAVs). ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Discriminant analysis

Controlled terms: Antennas? - ?Backpropagation? - ?Convolutional neural networks? - ?Grain (agricultural product)? - ?Infrared radiation? - ?Long short-term memory? - ?Mathematical transformations? - ?Particle swarm optimization (PSO)? - ?Remote sensing? - ?Sampling ? - ?Unmanned aerial vehicles (UAV)

Uncontrolled terms: HyperSpectral? - ?Maize straw? - ?Particle swarm? - ?Recognition? - ?Sensitive band? - ?Spectra’s? - ?Spectral characteristics? - ?Spectral transformations? - ?Stepwise discriminant analysis? - ?Upright maize straw

Classification code: 652.1 Aircraft? - ?716.5.1 Antennas? - ?731.1 Control Systems? - ?741.1 Light/Optics? - ?821.5 Agricultural Products? - ?903.1 Information Sources and Analysis? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1201.3 Mathematical Transformations? - ?1201.7 Optimization Techniques? - ?1202 Statistical Methods

Numerical data indexing: Percentage 8.90E+01%

DOI: 10.6041/j.issn.1000-1298.2025.11.037

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2026 Elsevier Inc.