2025年第8期共收录69篇

1. Spatiotemporal Evolution of Evapotranspiration and Contribution of Environmental Factors in Loess Plateau Agricultural Region

Accession number: 20253519069880

Title of translation: 黄土高原农业区蒸散发时空演变与环境因子贡献研究

Authors: Zheng, Meijun (1); Zha, Yuanyuan (1); Du, Shuai (1)

Author affiliation: (1) State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan; 430072, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 52-61

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The Loess Plateau agricultural area, a critical agricultural production zone in China, is characterized by severe water scarcity and ecological vulnerability. To support water resource management and ecological restoration, a systematic study was conducted on the spatiotemporal evolution of evapotranspiration (ET) and its driving mechanisms. Based on MODIS satellite data (2005-2020) , key environmental factors were integrated, including leaf area index ( LAI) , precipitation ( PRE ) , air temperature (Ta) , soil moisture ( SM) , and surface solar radiation (SSR). The following results were obtained, the spatial distribution of annual and summer ET exhibited a northwest-to-southeast increasing gradient, with 91.46% and 88.41% of the area showing a significant upward trend (p ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 43

Main heading: Soil moisture

Controlled terms: Abiotic? - ?Agriculture? - ?Ecosystems? - ?Landforms? - ?Landsat? - ?Restoration? - ?Sediments? - ?Soil surveys? - ?Water conservation? - ?Water management

Uncontrolled terms: %moisture? - ?Agricultural areas? - ?Air temperature? - ?Environmental factors? - ?Evapotranspiration? - ?Leaf Area Index? - ?Loess Plateau? - ?Loess plateau agricultural region? - ?Ridge regression? - ?Spatiotemporal evolution

Classification code: 405.3 Surveying? - ?444 Water Resources? - ?481.1 Geology? - ?483 Soil Mechanics and Foundations? - ?483.1 Soils and Soil Mechanics? - ?655.1 Satellites? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?913.5 Maintenance? - ?1501.2.1 Resource Conservation? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 2.328E+01%, Percentage 5.592E+01%, Percentage 8.841E+01%, Percentage 9.146E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.005

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

2. Farmland C02 Emission Flux Inversion Model Based on Variable Optimization and Machine Learning Algorithm

Accession number: 20253519059711

Title of translation: 基于变量优选与机器学习的农田 CO2 排放通量反演模型研究

Authors: Zhao, Wenju (1, 2); Ding, Lei (1, 2); Yu, Haiying (1, 2); Ma, Hong (1, 2); Zeng, Kai (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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 398-410

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to accurately obtain farmland C02 emission flux and accurately monitor greenhouse gases, the measured data of C02 were collected. Based on the spectral image data, the spectral reflectance of each sampling point was extracted, and the red edge band was introduced to improve the spectral index. The feature variables selected by variable importance in projection (VIP), Pearson correlation coefficient (PCC) and grey relational analysis (GRA) were used as the model input group. Based on the lightweight gradient boosting machine (LightGBM), back-propagation neural network (BPNN) and random forest (RF) machine learning algorithms, totally 36 C02 emission flux inversion models of tomato farmland at different growth stages were constructed. The results showed that the accuracy of the model constructed by PCC — GRA variable selection method was better than that of VIP and PCC methods. The inversion effect of LightGBM was better than that of BPNN and RF models. The inversion results can truly reflect the C02 emission flux of tomato farmland at different growth stages. Comparing the inversion accuracy of different models in each growth period, the inversion effect of LightGBM in growth period, flowering and fruit setting period and mature period was better than that of other models. The validation set determination coefficients R were 0.741, 0.818 and 0.779, respectively, and the root mean square errors (RMSE) were 0.035 mg/(m -h), 0.040 mg/(m -h) and 0. 229 mg/(m ? h), respectively. The mean absolute errors (MAE) were 0. 028 mg/(m ? h), 0.034 mg/(m *h) and 0.022 mg/(m -h), respectively. The inversion accuracy of the flowering and fruit setting period was the best. In the fruit enlargement period, the RF inversion effect was better than that of other models, R2p was 0.767, RMSEp was 0.031 mg/(m2-h), MAEp was 0.360 mg/(m2-h), and the dynamic change map of C02 emission flux in the whole growth period based on the best inversion model PCC — GRA — LightGBM can truly reflect the change characteristics of C02 emission flux in the study area. The results can provide a theoretical basis for the fine monitoring and estimation of farmland C02 emission flux. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Fruits

Controlled terms: Agricultural machinery? - ?Backpropagation? - ?Correlation methods? - ?Farms? - ?Greenhouse gases? - ?Learning systems? - ?Mean square error? - ?Random errors? - ?Random forests? - ?Reflection

Uncontrolled terms: C02 emission flux from farmland? - ?Emissions fluxes? - ?Gradient boosting? - ?Grey relational analysis? - ?Inversion models? - ?Lightgbm? - ?Optimisations? - ?Pearson correlation coefficients? - ?Spectral indices? - ?Variable optimization

Classification code: 731.1.1 Error Handling? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?1101.2 Machine Learning? - ?1202.2 Mathematical Statistics? - ?1301.3 Optics? - ?1502.1.2 Climate Change

Numerical data indexing: Mass 2.20E-08kg, Mass 2.29E-04kg, Mass 2.80E-05kg, Mass 3.10E-08kg, Mass 3.40E-08kg, Mass 3.50E-08kg, Mass 3.60E-07kg, Mass 4.00E-08kg

DOI: 10.6041/j.issn.1000-1298.2025.08.037

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

3. Advancements in Perception and Application of Agricultural Water Resources Information Driven by Multi-source Remote Sensing Data

Accession number: 20253519069953

Title of translation: 多源遥感数据驱动的农业水利信息感知与应用研究进展

Authors: Zhang, Zhitao (1, 2); Liu, Yanfu (1, 3); Hu, Xiaotao (1, 3); Chen, Junying (1, 2); Bian, Jiang (1, 2); Yang, Xiaofei (1, 3); Qian, Long (1, 3)

Author affiliation: (1) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Xinjiang Research Institute of Agriculture in Arid Areas, Nothwest A&F University, Urumqi; 830091, China; (3) Key Laboratory of Agricultural Soil and Water Engineering in Arid Areas, Northwest A&F University, Ministry of Education, Shaanxi, Yangling; 712100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 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: Agricultural water management is a critical component in ensuring global food security and the sustainable use of water resources, necessitating efficient and precise information sensing and regulation methods. In recent years, the integrated “ sky - space - ground” multi-source remote sensing observation system has provided opportunities for the dynamic monitoring of agricultural water resources, particularly at the regional and field scales. The latest research advancements in the application of multi-source remote sensing data for agricultural water resources perception, covering data acquisition and processing, modeling methods, and typical applications were systematically reviewed. In terms of data acquisition, the collaboration between satellite, drone, and ground-based platform sensors significantly enhanced data spatial resolution and observation dimensions. Regarding data processing, remote sensing data processing was transitioning from localized approaches to cloud-based collaborative processing, thereby improving data fusion efficiency and spatiotemporal consistency. In modeling, hybrid models combining physical mechanisms and data-driven approaches were becoming mainstream, significantly improving predictive accuracy and model generalization. These advancements drove the widespread application of remote sensing technologies in agricultural drought and flood monitoring, crop growth status assessment, and environmental monitoring. However, despite significant progress in multi-source remote sensing technology, several challenges remained in its application for agricultural water resources perception. These challenges included difficulties in information integration between platforms, lack of standardization in data processing, room for improvement in model performance, and the need to enhance the conversion and service capabilities of research outcomes. Looking ahead, future research should focus on building high spatiotemporal collaborative observation systems, developing platform-based and intelligent data processing workflows, promoting modeling methods that integrated mechanisms with intelligence, and deepening the fusion of remote sensing services with practical application scenarios, aiming to provide stronger support for the realization of smart agriculture and the achievement of sustainable development goals. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 115

Main heading: Sustainable development

Controlled terms: Data accuracy? - ?Data acquisition? - ?Data integration? - ?Environmental technology? - ?Information management? - ?Remote sensing? - ?Smart agriculture? - ?Space optics? - ?Sustainable development goals? - ?Water resources

Uncontrolled terms: Agricultural water? - ?Agricultural water resource? - ?Information perception? - ?Multi-source remote sensing? - ?Multi-Sources? - ?Remote sensing data? - ?Remote-sensing? - ?Sky - space - ground integration? - ?Smart agricultures? - ?Waters resources

Classification code: 444 Water Resources? - ?655.2 Spacecraft Subsystems? - ?731.1 Control Systems? - ?821.4 Agricultural Methods? - ?903 Information Science? - ?1106.2 Data Handling and Data Processing? - ?1501.1 Sustainable Development? - ?1502 Environmental Engineering

DOI: 10.6041/j.issn.1000-1298.2025.08.001

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

4. Design and Experiment of High-speed Air-assisted Bi-variable Seed Supply Device for Oilseed Rape

Accession number: 20253519063673

Title of translation: 油菜高速气送式双变量供种装置设计与试验

Authors: Zhang, Wenxin (1); Li, Haopeng (1); Dong, Wanjing (1, 2); Zhang, Dongjin (1); Yu, Qiuli (1); Ding, Youchun (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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 252-264

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the adaptability of the seed supply device at different operating speeds and the lack of seed supply precision in the existing air-assisted direct seeding operations for oilseed rape, a high-speed air-fed bi-variable seed supply device for oilseed rape was designed. This device achieved a wide range of precise seed supply by combining “variable aperture length + variable rotational speed”. Based on elucidating the working principle of the seed supply device, a three-stage kinematic model of seed supply-carrying-dropping was established. Key structural and operational parameters, such as aperture structure, rotational speed of the seed metering groove wheel, and aperture length were determined. Through bench tests, the relationship model among rotational speed, aperture length, and seed supply rate was studied, and a bi-variable control strategy for seed metering rotational speed and aperture length was constructed at different sowing rates and operating speeds. Comparative experiments on single-variable and bi-variable seed supply performance were conducted. The bench test results showed that the bi-variable seed supply device achieved a seed supply accuracy of no less than 93. 3% within the rotational speed range of 15 ? 70 r/min, which was 8. 2 percentage points higher than that of the single-variable type. The seed supply rate ranged for the single-variable and bi-variable types were 35. 7 ? 123. 2g/min and 37.3?286.0 g/min, respectively. The bi-variable seed supply device demonstrated higher seed supply precision within the range of 2. 25 ? 6 kg/hm2. Furthermore, the road test results for sowing performance indicated that, at the same seeding rate and different operating speeds, the seed supply accuracy ranged from 90. 61% to 97. 69%, and the coefficient of variation for the uniformity of sowing rate among rows ranged from 1.69% to 3.90%, meeting the national standards and relevant industry requirements. Field test results showed that, within the sowing rate range of 2. 25 ? 5. 25 kg/hm2 and operating speeds of 3 ? 12 km/h, the seed supply accuracy can reach up to 97. 6%. The coefficients of variation for the consistency of sowing rate among rows and the stability of sowing rate were no more than 11.1% and 9. 5%, respectively. The bi-variable seed supply device can meet the requirements for precision seed supply in air-assisted direct seeding operations for oilseed rape. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Oilseeds

Controlled terms: Kinematics

Uncontrolled terms: Bi-variable seed supply device? - ?Direct-seeding? - ?High Speed? - ?Oil seed rape? - ?Operating speed? - ?Performance tests? - ?Precision seed supply? - ?Rotational speed? - ?Seed metering? - ?Single variable

Classification code: 821.5 Agricultural Products? - ?1301.1.1 Mechanics

Numerical data indexing: Angular velocity 1.169E+00rad/s, Mass 2.50E+01kg, Mass 6.00E+00kg, Mass flow rate 3.34E-05kg/s, Mass flow rate 4.7762E-03kg/s, Percentage 1.11E+01%, Percentage 1.69E+00% to 3.90E+00%, Percentage 3.00E+00%, Percentage 5.00E+00%, Percentage 6.00E+00%, Percentage 6.10E+01% to 9.70E+01%, Percentage 6.90E+01%, Size 1.20E+04m

DOI: 10.6041/j.issn.1000-1298.2025.08.023

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

5. One-dimensional Unsteady Heat Transfer Model of Greenhouse Soil Considering Boundary Condition Prediction

Accession number: 20253519059790

Title of translation: 考虑边界条件预测的温室土壤一维非稳态传热模型构建

Authors: Zhang, Guanshan (1, 2); Ding, Xiaoming (1, 3); Chen, Yuefeng (4); Dong, Wei (2); Yin, Yilei (1, 3); Li, Tianhua (2); Qi, Fei (1, 3); Fan, H.E. (1, 3)

Author affiliation: (1) Academy of Agricultural Planning and Engineering, Ministry of Agriculture and Rural Affairs, Beijing; 100125, China; (2) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China; (3) Key Laboratory of Farm Building in Structure and Intelligent Construction, Ministry of Agriculture and Rural Affairs, Beijing; 100125, China; (4) Zhejiang Branch of Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Shaoxing; 312039, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 634-643

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Soil temperature is one of the important factors affecting crop growth and greenhouse thermal environment. In order to accurately predict the greenhouse soil temperature, a one-dimensional unsteady heat transfer model of soil was constructed by using theory of computational fluid dynamics. In order to solve the difficult problem of obtaining boundary condition, a boundary condition prediction model was constructed by using long short term memory (LSTM) neural network. Accuracy verification test of one-dimensional unsteady soil heat transfer model was firstly carried out with boundary condition measured by sensor. The results showed that the variation trend of calculated and measured soil temperature in different seasons and depths were consistent. The maximum value of mean absolute error (MAE) and max absolute error (MaxAE) between predicted and measured soil temperature were 1. 29 Tl and 2. 16T1, respectively. Secondly, the prediction model of boundary condition was verified. The results showed that the determination coefficient (R) between predicted and measured value of boundary condition was 0. 99. The maximum value of MAE and MaxAE between predicted and measured value of boundary condition were 0. 18^ and 2. 63t, respectively. The results indicated that the model could accurately predict the boundary condition. Finally, the predicted and measured boundary condition data were introduced into one-dimensional unsteady heat transfer model of soil, respectively. The calculated results of the model with measured and predicted boundary condition were compared with the measured soil data. The results showed that the simulation results of soil temperature with predicted and measured boundary condition were consistent. The maximum deviation of R, MAE and MaxAE between calculated and measured soil temperature under measured and calculated soil boundary condition were 0. 03, 0. 14T1 and 0. 92^, respectively. The above results showed that one-dimensional unsteady heat transfer model of soil under predicted boundary condition can predict the soil temperature in different depths accurately. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Boundary conditions

Controlled terms: Computation theory? - ?Computational fluid dynamics? - ?Forecasting? - ?Greenhouses? - ?Heat transfer? - ?Long short-term memory? - ?One dimensional? - ?Soil surveys? - ?Soil temperature? - ?Soil testing ? - ?Soils

Uncontrolled terms: Absolute error? - ?Condition? - ?Condition prediction? - ?Greenhouse soil? - ?Heat transfer model? - ?Mean absolute error? - ?One-dimensional? - ?Short term memory? - ?Soil temperature? - ?Unsteady heat transfer

Classification code: 301.1 Fluid Flow? - ?301.1.4 Computational Fluid Dynamics? - ?302 Thermodynamics and Heat Transfer? - ?302.2 Heat Transfer? - ?405.3 Surveying? - ?483.1 Soils and Soil Mechanics? - ?821.7 Farm Buildings and Other Structures? - ?1101.2.1 Deep Learning? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1201.12 Modeling and Simulation? - ?1201.14 Geometry and Topology? - ?1202 Statistical Methods? - ?1502.1.1.4.3 Soil Pollution Control

DOI: 10.6041/j.issn.1000-1298.2025.08.060

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

6. Research Progress of Detection and Grading Methods for Major Grapevine Diseases

Accession number: 20253519072380

Title of translation: 葡萄主要病害检测与分级方法研究进展

Authors: Zhai, Changyuan (1, 2); Liu, Bohao (1, 2); Li, Cuiling (2, 3); Zhao, Xueguan (2, 3); Liu, Haowei (2); Hao, Jianjun (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding; 071001, China; (2) Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (3) National Engineering Research Center for Information Technology in Agriculture, Beijing; 100097, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 341-359

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Grapevines face significant threats from various diseases during their growth cycle, including downy mildew, powdery mildew, botrytis cinerea, anthracnose, and black rot. Current reliance on extensive chemical spraying for disease control has led to persistent environmental challenges such as soil contamination and pesticide residues, resulting in ecological degradation of vineyards and economic losses. This necessitates the development of classified and hierarchical management strategies for major grape diseases. The contemporary research progress in grape disease identification, detection, and classification methodologies was systematically examined, with particular emphasis on latent period detection and symptomatic phase assessment technologies. The analysis encompassed molecular biological detection, spectral analysis, imaging techniques, unmanned aerial vehicles (UAVs), satellite remote sensing, and multi-source data fusion approaches, complemented by machine learning and deep learning-based recognition systems for disease classification, object detection, and severity grading. Critical challenges were identified in current research paradigms, including limited applicability of detection methods across diverse cultivation scenarios, insufficient generalizability of classification models, technical barriers in on-site multi-modal and multi-disease collaborative detection, incomplete understanding of dynamic pathological progression, and reduced recognition accuracy for diseases with similar symptomatic expressions. The study further elucidated the underlying causes of these limitations through comparative analysis of recent algorithmic advancements in disease detection, evaluating performance metrics and optimization strategies. Future research directions emphasized the development of intelligent detection systems for complex natural environments, establishment of generalizable detection-classification frameworks, small-sample learning and fine-grained recognition techniques, dynamic disease progression tracking with lesion prediction capabilities, and integrated pest management equipment incorporating diagnostic data. These advancements aimed to establish a technological foundation for precision phytopharmaceutical applications and intelligent disease management in modern viticulture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 143

Main heading: Grading

Controlled terms: Agricultural machinery? - ?Antennas? - ?Cultivation? - ?Diagnosis? - ?Disease control? - ?Diseases? - ?Error detection? - ?Learning systems? - ?Losses? - ?Machine learning ? - ?Object detection? - ?Pesticides? - ?Plant diseases? - ?Remote sensing? - ?Research and development management? - ?Soil pollution? - ?Spectrum analysis? - ?Unmanned aerial vehicles (UAV)

Uncontrolled terms: ‘current? - ?Black rot? - ?Botrytis cinerea? - ?Detection? - ?Detection methods? - ?Downy mildew? - ?Grading methods? - ?Grape? - ?Growth cycle? - ?Powdery mildew

Classification code: 102.1 Medicine? - ?102.1.2 Health Science? - ?103 Biology? - ?652.1 Aircraft? - ?703 Electric Circuits? - ?716.5.1 Antennas? - ?731.1 Control Systems? - ?731.1.1 Error Handling? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals? - ?821.4 Agricultural Methods? - ?901.3 Engineering Research? - ?911.2 Industrial Economics? - ?912.2 Management? - ?1101.2 Machine Learning? - ?1106.8 Computer Vision? - ?1301.1.3.1 Spectroscopy? - ?1502.1.1.3 Soil Pollution

DOI: 10.6041/j.issn.1000-1298.2025.08.032

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

7. Design and Test of Rice and Wheat Combined Seeder of Sowing Depth Automatic Control

Accession number: 20253519069942

Title of translation: 播深自动调控稻麦联合播种机设计与试验

Authors: Yong, Kai (1); Wang, Gang (2); Wang, Zhengbing (1); Zuo, Liming (1); Yang, Ya (1)

Author affiliation: (1) Wuhu Institute of Technology, School of Smart Manufacturing, Wuhu; 241006, China; (2) Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing; 210014, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 265-273

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: For the problem of unstable sowing depth of rice and wheat seeds due to the unevenness of the fields in the middle and lower reaches of the Yangtze River, the 2BFGL - 19 sowing depth automatic regulation rice and wheat combined seeder was developed, which can monitor and automatically adjust the sowing depth in real time. The key components such as the sowing depth control device and the sowing depth monitoring device were designed, and the hydraulic transmission and control system was established based on the principle of sowing depth automatic control. Through the motion analysis of the sowing depth monitoring device on both sides, the relation between the sloping ground and the distance from the touch plate to the front and rear touch modules was obtained. On the premise that the critical angle of the sloping ground was 3°, the distance from the touch plate to the front and rear touch modules ware 19. 07 mm and 12. 29 mm. The coefficient of variation of the consistency of the sowing amount of each row was taken as the evaluation index, the standard dynamic test was carried out. The test results showed that when the test machine worked at speed of 0.6 m/s to 1.2 m/s, the average coefficient of variation of the consistency of the sowing amount of each row was 3. 44% , and the sowing amount consistency of the machine was good. Taking into account the working efficiency, when the working speed was 0. 8 m/s, the coefficient of variation of consistency of sowing amount of each row of the test machine was 3. 24% , which was better than 3. 62% of the prototype machine, and the test machine had better sowing stability at this speed section. The qualified rate of sowing depth and the coefficient of variation of sowing uniformity was taken as the evaluation index. The field test was carried out. The test results showed that the qualified rate of sowing depth was 87. 04% , the average coefficient of variation of sowing uniformity was 22. 86% . All indexes met the operation quality of grain drill standards, compared with the prototype machine, the comprehensive performance of the test machine was improved. The test results met the design requirements. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Automation

Controlled terms: Agricultural machinery? - ?Grain (agricultural product)? - ?Hydraulic equipment? - ?Hydraulic machinery? - ?Motion analysis? - ?Process control? - ?Seed

Uncontrolled terms: Coefficients of variations? - ?Depth control? - ?Evaluation index? - ?Hydraulic system? - ?Monitoring device? - ?Rice and wheat seeder? - ?Sloping ground? - ?Sowing depth control? - ?Sowing depth monitoring? - ?Test machine

Classification code: 731 Automatic Control Principles and Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?913.3 Quality Assurance and Control? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1401.2 Hydraulic Equipment and Machinery

Numerical data indexing: Percentage 2.40E+01%, Percentage 4.00E+00%, Percentage 4.40E+01%, Percentage 6.20E+01%, Percentage 8.60E+01%, Size 2.90E-02m, Size 7.00E-03m, Velocity 6.00E-01m/s to 1.20E+00m/s, Velocity 8.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2025.08.024

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

8. Effect of Nitrogen Reduction with Biochar Application on Soda Saline Soil Environment and Growth Characteristics of Soybean

Accession number: 20253519059796

Title of translation: 减氮配施生物炭对苏打盐碱土壤环境与大豆生长特性的影响

Authors: Yang, Aizheng (1); Li, Hongyu (1); Wang, Qiuju (2); Xu, Bo (1); Sha, Yan (1); Li, Mo (1)

Author affiliation: (1) School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin; 150030, China; (2) Heilongjiang Province Black Soil Protection, Utilization Research Institute, Harbin; 150030, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 602-613

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Soil salinization is a critical factor constraining the sustainable agricultural development of sodic saline soils in black soil regions. The effects of nitrogen reduction with biochar and its application depth on soda saline soil environment and soybean growth were investigated through a pot experiment. Two biochar application levels; 0 t/hm (B0), 4. 5 t/hm (Bl), two biochar application depths; 0 ~ 20 cm (HI), 0 ~ 40 cm (H2), and three nitrogen application levels; conventional nitrogen application (NO), 15% nitrogen reduction (Nl), and 30% nitrogen reduction (N2) were set. The experimental results showed that under the condition of nitrogen reduction, the application of biochar could improve the saline soil environment and significantly increase the soybean yield. At the same time, with the increase of the depth of biochar application, the effect of biochar in reducing saline soil barriers was significantly increased, which in turn promoted the growth of soybean. Compared with BONO treatment, B1H1N1 treatment had the best soil improvement effect in 0 ~ 20 cm soil layer, in which K+ content was increased by 20.54%, Na+ content was decreased by 24.30%, sodium adsorption ratio was decreased by 46. 68%, and nitrate nitrogen was increased by 26. 61%, and B1H2N1 treatment had the best effect of improving the soil physicochemical properties in 20 ~40 cm soil layer, in which K+ content was increased by 18.64%, Na content was decreased by 21.71%, sodium adsorption ratio was decreased by 32.85%, and nitrate nitrogen was increased by 30.77%. The B1H2N1 treatment improved leaf N content by 21. 54%, leaf N nutrient accumulation by 33. 61%, soybean grain weight by 3. 33%, and N fertilizer productive efficiency by 28. 37% compared with the BONO. Combined with the comprehensive evaluation results of entropy-weighted TOPSIS model, B1H2N1 treatment had the best comprehensive performance, which can activate the deep soil resources and improve the utilization rate of nitrogen fertilizer while effectively regulating the distribution of salinity in soda saline soil and providing a light-salt environment for crop growth. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 41

Main heading: Efficiency

Controlled terms: Entropy? - ?Nitrogen? - ?Nitrogen fertilizers? - ?Plants (botany)? - ?Reduction? - ?Sodium? - ?Sodium chloride? - ?Soil quality? - ?Sustainable development

Uncontrolled terms: Biochar? - ?Entropy weight TOPSIS model? - ?Entropy weights? - ?Nitrogen reduction? - ?Nitrogen-use efficiency? - ?Saline soil? - ?Soda-saline soil? - ?Soil environment? - ?Soybean? - ?TOPSIS models

Classification code: 103 Biology? - ?202.9.1 Alkali Metals? - ?302.1 Thermodynamics? - ?482.1 Minerals? - ?483.1 Soils and Soil Mechanics? - ?802.2 Chemical Reactions? - ?804 Chemical Products? - ?804.2 Inorganic Compounds? - ?821.3 Agricultural Chemicals? - ?913.1 Production Engineering? - ?1501.1 Sustainable Development

Numerical data indexing: Percentage 1.50E+01%, Percentage 1.864E+01%, Percentage 2.054E+01%, Percentage 2.171E+01%, Percentage 2.43E+01%, Percentage 3.00E+01%, Percentage 3.077E+01%, Percentage 3.285E+01%, Percentage 3.30E+01%, Percentage 3.70E+01%, Percentage 5.40E+01%, Percentage 6.10E+01%, Percentage 6.80E+01%, Size 0.00E00m to 2.00E-01m, Size 0.00E00m to 4.00E-01m, Size 2.00E-01m to 4.00E-01m

DOI: 10.6041/j.issn.1000-1298.2025.08.057

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

9. Target Recognition Method for Apple Picking Robot at Night Based on Self-correcting Illumination Network

Accession number: 20253519059776

Title of translation: 基于自校正照明网络的苹果采摘机器人夜间目标识别方法

Authors: Xu, Xianghu (1, 2); Yuan, Minxin (1, 2); Cheng, Zeyuan (1, 2); Xue, Wenyu (1, 2); Wang, Zheng (2, 3); Yang, Fuzeng (1, 2)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Apple Full Mechanized Scientific Research Base, Ministry of Agriculture and Rural Affairs, Shaanxi, Yangling; 712100, China; (3) College of Forestry, Northwest A&F University, Shaanxi, Yangling; 712100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 447-457

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Nighttime picking by robots is an effective way to significantly improve the operational efficiency of apple-picking robots. To solve the key problems such as the inability of apple-picking robots to accurately identify apples and distinguish different targets in the night environment, a night target recognition method for apple-picking robots was proposed based on a self-correcting illumination network. Based on the TracePro software, the illumination uniformity of the image acquisition area was analyzed, and the optimal installation angle of the compensation light source was determined. By comparing and analyzing the variation laws of the RGB and HSV component values of images under four compensation light sources, namely incandescent lamps, LED lamps, high-pressure sodium lamps, and halogen lamps, it was determined that incandescent lamps were the optimal compensation light sources suitable for the night recognition and detection of apple targets. YOLO v8s was selected as the backbone network, and the self-correcting illumination network SCINet was embedded into the YOLO v8 network, which solved the problems of local overexposure and local underexposure of the image. The attention mechanism CBAM was embeded into the backbone network to better extract the features of apple targets; the original UpSample module was replaced with the ultra-lightweight dynamic upsampler DySample module to enhance the detection ability of small targets such as apples deep in the tree canopy. Training and testing were conducted on the constructed nighttime apple dataset. The test results showed that the improved YOLO v8 model can achieve the recognition and classification of picking and non-picking apples in images under the night environment. The recall rate of the recognition model was 82. 9%, the accuracy rate was 81. 4%, and the mAP was 86. 8% . The model was compared with mainstream models such as YOLO v5s, YOLO v7, YOLO v8n, YOLO v8s, YOLO v8m, and YOLO vlls. The mAP of the improved YOLO v8s model was increased by 3. 9, 13. 1, 12. 9, 6. 2, 2. 7 and 5. 7 percentage points respectively. The recall rates were increased by 0. 9, 6. 1, 7.2, 2. 6, 2. 3 and 1. 8 percentage points respectively. The research results can provide technical support for the apple-picking robot’s operations in the night environment. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Fruits

Controlled terms: Environmental testing? - ?Forestry? - ?Incandescent lamps? - ?Robots? - ?Security systems? - ?Statistical tests? - ?Target tracking

Uncontrolled terms: Apple picking robot? - ?Back-bone network? - ?Nighttime compensated light source? - ?Nighttime recognition? - ?Picking robot? - ?Recall rate? - ?Recognition methods? - ?Self-correcting illumination network? - ?Target recognition? - ?Targets detection

Classification code: 435.2 Tracking and Positioning? - ?707.2 Electric Lamps? - ?731.5 Robotics? - ?821.1 Woodlands and Forestry? - ?821.5 Agricultural Products? - ?914.1 Accidents and Accident Prevention? - ?1108 Security and Privacy? - ?1202.2 Mathematical Statistics? - ?1502.1 Environmental Impact and Protection

Numerical data indexing: Percentage 4.00E+00%, Percentage 8.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.08.042

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

10. Multi-step Water Temperature Prediction Model for Cephalopholis sonnerati Based on IHO-Mamba-MHSA

Accession number: 20253519059782

Title of translation: 基于 IHO-Mamba-MHSA 的红瓜子斑鱼养殖水温多步预测模型

Authors: Xu, Longqin (1, 2); He, Min (1); Chen, Ziang (1); Che, Zhuhong (1); Pang, Huiyuan (1); Huang, Tianyou (1); Li, Honglei (3); Liu, Shuangyin (1, 4)

Author affiliation: (1) College of Artificial Intelligence, Zhonghai University of Agriculture and Engineering, Guangzhou; 510225, China; (2) Academy of Intelligent Agricultural Engineering Innovations, Zhonghai University of Agriculture and Engineering, Guangzhou; 510225, China; (3) Qingdao Ruichuang Leading Science and Technology Internet oj Things Co, Ltd., Qingdao; 266100, China; (4) Intelligent Agriculture Engineering Technology Research Center of Guangdong Higher Education Institutes, Guangzhou; 510225, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 655-664

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to improve the accuracy of water temperature prediction in industrial Cephalopholis sonnerati aquaculture, a multi-step prediction model, IHO — Mamba — MHSA, which integrated the improved hippopotamus optimization algorithm (IHO), the Mamba model, and the multi-head self-attention (MHSA) mechanism was proposed. The interquartile range (IQR) method identified outliers, and linear interpolation imputed missing values to reduce noise impact. Key feature selection was performed by using extreme gradient boosting (XGBoost) . To enhance the global and local search capabilities of the hippopotamus optimization algorithm (HO) and improve its convergence speed, an IHO was proposed by incorporating differential mutation, Levy flight, and cauchy mutation to optimize a multi-objective algorithm. To further strengthen the model’s ability to capture nonlinear relationships in water temperature, handle multi-step dependencies, and extract global information, a predictive framework combining the Mamba model and MHSA was introduced. The IHO was used to optimize the hyperparameters of the Mamba — MHSA model, forming the IHO — Mamba— MHSA multi-step prediction model for industrial Cephalopholis sonnerati aquaculture water temperature. The proposed model was validated by using water temperature data from an industrial aquaculture facility in Laizhou, Shandong. Compared with genetic algorithm (GA), particle swarm optimization (PSO), and the standard HO, the IHO achieved the highest reductions in MAE, MSE, and MAPE by 33. 33%, 21. 74%, and 18. 37%, respectively, while increasing R by up to 4.42%, demonstrating its superior multi-parameter optimization performance. Furthermore, compared with long short-term memory (LSTM), gated recurrent unit (GRU), backpropagation neural network (BPNN), and temporal convolutional network (TCN), the proposed model consistently outperformed across different forecasting horizons, maintaining an R as high as 0. 888 even at a 24-step horizon. The experimental results indicated that the proposed model met the requirements for precise water temperature prediction and refined management in industrial aquaculture, providing valuable insights for water quality regulation in intensive fish farming systems. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Particle swarm optimization (PSO)

Controlled terms: Aquaculture? - ?Backpropagation? - ?Convolution? - ?Convolutional neural networks? - ?Forecasting? - ?Genetic algorithms? - ?Interpolation? - ?Long short-term memory? - ?Maximum likelihood? - ?Prediction models ? - ?Temperature

Uncontrolled terms: Cephalopholi sonnerati? - ?Improved hippopotamus optimization algorithm? - ?Industrial aquaculture? - ?Mamba model? - ?Multi-step water temperature prediction? - ?Multisteps? - ?Optimization algorithms? - ?Prediction modelling? - ?Temperature prediction? - ?Water temperatures

Classification code: 302.1 Thermodynamics? - ?716.1 Information Theory and Signal Processing? - ?821.4 Agricultural Methods? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1201.7 Optimization Techniques? - ?1201.9 Numerical Methods? - ?1202 Statistical Methods

Numerical data indexing: Percentage 3.30E+01%, Percentage 3.70E+01%, Percentage 4.42E+00%, Percentage 7.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.062

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

11. Review of Sorting Technologies and Equipment for Thin-skinned Fruits and Vegetables

Accession number: 20253519059787

Title of translation: 薄皮果蔬分选技术与设备研究进展与展望

Authors: Xiong, Wei (1, 2); Zhou, Lichen (1); Yu, Hao (1); Song, Shuai (1); Zhu, Dequan (1, 2)

Author affiliation: (1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) Anhui Provincial Engineering Laboratory for Intelligent Agricultural Machinery Equipment, Hefei; 230036, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 665-683

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Intelligent sorting of fruits and vegetables represents a core link in agricultural modernization and industrial value chain reconstruction, contributing to the reduction of post-harvest losses and the enhancement of fruit and vegetable quality. Thin-skinned fruits and vegetables, characterized by their special structures and diverse morphologies, pose significant challenges in detection and are prone to mechanical damage. Their sorting technologies reflect the development level of mechanization and modernization in primary agricultural product processing. To address this, a comprehensive review of the research status and development trends of sorting technologies and equipment for thin-skinned fruits and vegetables at home and abroad was presented. Focusing on the structural and morphological characteristics of thin-skinned fruits and vegetables, an in-depth analysis and summary of representative sorting technologies and equipment was conducted from different aspects, including biomechanical properties, sorting technology principles, design and application of key devices in sorting equipment. Particular emphasis was placed on innovative design methods for technical equipment aimed at reducing material damage and improving the detection accuracy of internal and external qualities of thin-skinned fruits and vegetables. Finally, targeting the existing problems in sorting technologies and equipment for thin-skinned fruits and vegetables in China, it was proposed that the adaptability to irregularly shaped fruits and vegetables, the universality and personalization of detection models, and lightweight, efficient, and fully automated equipment would be important development directions for sorting technologies and equipment for thin-skinned fruits and vegetables in the future. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 161

Main heading: Fruits

Controlled terms: Agricultural machinery? - ?Design? - ?Modernization? - ?Screening? - ?Sorting

Uncontrolled terms: Agricultural modernizations? - ?Fruit and vegetables? - ?Full-surface information? - ?Machine-vision? - ?Sorting equipment? - ?Sorting technology? - ?Spectral detection? - ?Surface information? - ?Technology and equipments? - ?Thin-skinned fruit and vegetable

Classification code: 802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?901 Engineering Profession? - ?904 Design? - ?1106.2 Data Handling and Data Processing

DOI: 10.6041/j.issn.1000-1298.2025.08.063

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

12. Mixed Multi Pose Synthesis of Film-mulched Transplanting Mechanism for Rice Pot Seedlings Using GFPSO Algorithm

Accession number: 20253519069910

Title of translation: 基于改进粒子群优化算法的水稻钵苗膜上移栽机构混合多位姿综合与试验

Authors: Xin, Liang (1); Feng, Yuchen (1); Zhang, Yiqun (1); He, Zeyu (1)

Author affiliation: (1) College of Engineering, Northeast Agricultural University, Harbin; 150030, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 283-292

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issue that existing transplanting mechanisms for rice pot seedlings on film-mulched fields lack independent trajectory and posture control for seedling transplanting and film-breaking hole-digging operations, thereby affecting collaborative operation quality, a mixed multi-pose synthesis method was proposed based on an improved particle swarm optimization ( GFPSO ) algorithm for the design of a juxtaposed composite non-circular gear planetary gear train mechanism, aiming to develop an advanced transplanting mechanism for rice pot seedlings on film-mulched fields. Firstly, design requirements for the juxtaposed transplanting mechanism were established. Ideal trajectories for seedling transplanting and collaborative film-breaking hole-digging operations were planned, with key pose points selected. A mixed multi-pose synthesis model was developed for the mechanism. By integrating the particle swarm optimization algorithm with a fitness - distance balance selection strategy and Gaussian random walk diffusion, an enhanced GFPSO algorithm was proposed to solve the synthesis model, enabling the design of the composite non-circular planetary gear train. Based on the optimized results, a 3D model of the mechanism was constructed, and virtual prototype simulations were performed by using ADAMS. Comparative analysis of simulated trajectories and key pose parameters validated the design accuracy. Physical prototypes and test benches were subsequently fabricated for no-load experiments. Experimental results demonstrated close alignment between actual motion trajectories/postures and theoretical/virtual simulation outcomes. Further performance tests on rice pot seedling transplanting showed an average transplanting success rate of 90. 95% and an average plant spacing variation coefficient of 2. 35% , confirming the mechanism’ s feasibility and practicality for film-mulched field applications. The research result can provide a systematic methodology for the integrated design of trajectory and posture control in agricultural machinery, offering technical insights for enhancing precision and reliability in mechanized rice transplanting operations on film-mulched fields. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Machine design

Controlled terms: 3D modeling? - ?Agricultural machinery? - ?Gear trains? - ?Gears? - ?Seed? - ?Software prototyping? - ?Three dimensional computer graphics? - ?Trajectories

Uncontrolled terms: Composite non circular gear planetary gear system? - ?Improved particle swarm optimization? - ?Mixed multi pose synthesis? - ?Multi-pose? - ?Non-circular gears? - ?Particle swarm? - ?Planetary gear systems? - ?Pose synthesis? - ?Swarm optimization? - ?Transplanting rice pot seedling on film-mulched

Classification code: 601 Mechanical Design? - ?601.2 Machine Components? - ?602 Mechanical Drives and Transmissions? - ?656 Space Flight and Research? - ?661 Automotive Engines and Related Equipment? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?902.1 Engineering Graphics? - ?904 Design? - ?1106.2 Data Handling and Data Processing? - ?1106.9 Computer Software? - ?1201.12 Modeling and Simulation

Numerical data indexing: Percentage 3.50E+01%, Percentage 9.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.026

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

13. Identification Method and Spatio-temporal Evolution of Surface Water Body in Heihe River Basin Based on Landsat Imagery

Accession number: 20253519069956

Title of translation: 基于Landsat的黑河流域水体识别方法与时空演变

Authors: Wenju, Zhao (1, 2); Zhendong, Xie (1, 2); Wen, Xu (3); Haiying, Yu (1, 2); Guolong, Zhan (2, 4); Zhijun, Wang (1, 2)

Author affiliation: (1) College of Energy and Power 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) Hydrology and Water Resources Center of Cansu Province, Lanzhou; 730030, China; (4) Dayu Water-saving (Tianjin) Co., Ltd., Tianjin; 301712, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 152-162

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of extracting water bodies in large-scale environments and clarifying their long-term evolution patterns, the Landsat images of Heihe River Basin (1986-2024) were processed by using Google Earth Engine ( GEE ) , approximately 78 000 water and non-water samples were collected, and an annual sample dataset was built. Based on this dataset, a random forest (RF)-based water extraction method was developed by integrating the multi band water index (MBWI) , enhanced water index (EWI) , and modified normalized difference water index (MNDWI) with spectral bands, both individually and in combination. Through systematic screening, the optimal fusion index was selected, enabling the accurate extraction of surface water bodies across 39 temporal phases. The Mann - Kendall ( M - K) test was applied to detect interannual trends in surface water area, while principal component analysis ( PCA) and sensitivity analysis were used to identify the dominant driving factors influencing water body evolution. The results demonstrated that the RF method incorporating all three water indices (MBWI, EWI, and MNDWI) achieved the best extraction performance for Landsat images of the Heihe River Basin, with average overall accuracy (OA) of 96. 16% and average Kappa coefficient ( KC ) of 0. 912 8. The M - K test indicated a fluctuating downward trend in surface water area from 1986 to 2024. Annual precipitation, population, and annual evapotranspiration were identified as the main driving factors for the evolution of surface water bodies in the Heihe River Basin. The research result can provide a theoretical foundation for the rapid and accurate extraction of surface water bodies at the basin scale and support future hydrological and environmental applications. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 45

Main heading: Sensitivity analysis

Controlled terms: Evapotranspiration? - ?Extraction? - ?Landsat? - ?Long Term Evolution (LTE)? - ?Principal component analysis? - ?Rivers? - ?Watersheds

Uncontrolled terms: Heihe river basin? - ?Landsat images? - ?Landsat satellite? - ?Multi band? - ?Normalized difference water index? - ?Spatiotemporal evolution? - ?Surface water body? - ?Water body identification? - ?Water index? - ?Waterbodies

Classification code: 407 Maritime and Port Structures; Rivers and Other Waterways? - ?444 Water Resources? - ?655.1 Satellites? - ?716 Telecommunication; Radar, Radio and Television? - ?802.3 Chemical Operations? - ?1101.2 Machine Learning? - ?1201 Mathematics? - ?1502.3 Hydrology

Numerical data indexing: Percentage 1.60E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.014

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

14. Collision Avoidance Control for Robots Based on Human-Robot Collaboration

Accession number: 20253519059775

Title of translation: 基于人机协作的机器人防撞控制研究

Authors: Wang, Zhijun (1, 2); Ma, Wenwen (1); Yang, Yue (1); Feng, Yongli (1, 2); Li, Zhanxian (1, 2)

Author affiliation: (1) College of Mechanical Engineering, North China University of Science and Technology, Tangshan; 063210, China; (2) Industrial Robotics Research Institute of Hebei Province, Tangshan; 063210, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 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: Aiming at the unpredictable collision problem between human and robot end in human — robot cooperative environment, a double-loop human — robot cooperative anti-collision control algorithm composed of adaptive admittance control and robust control was proposed. Firstly, based on the analysis of admittance parameters, an outer loop adaptive admittance controller was established, and the damping and stiffness parameters were updated in real time according to the collision force information to generate the avoidance trajectory. Then, according to the robust control theory, the inner loop robust tracking controller was established to overcome the influence of dynamics error on the trajectory tracking accuracy. Finally, the visual simulation and experimental verification of the algorithm were carried out. The simulation results showed that compared with the variable stiffness admittance control, the maximum offset of the end position on the x, y and z axes was increased by 17.46%, 8.89% and 13.09%, respectively, and the response time was reduced by 7. 94%, 14. 89% and 8. 21%, respectively, which significantly improved the flexibility of the robot end collision response. In the collision experiment, the maximum collision forces on the x, y and z axes of the robot end were 1. 12 N, 4. 06 N and -0.44 N, respectively. The maximum position offsets of the robot end in the direction of collision force were 6. 94 mm, 98. 75 mm and 5. 43 mm, respectively. The results showed that the robot end could show good flexibility under the action of small collision force, which met the safety requirements of human — robot cooperation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Robust control

Controlled terms: Adaptive control systems? - ?Collision avoidance? - ?Controllers? - ?Electric admittance? - ?Man machine systems? - ?Robots? - ?Stiffness? - ?Visual servoing

Uncontrolled terms: Adaptive admittance control? - ?Admittance control? - ?Collision forces? - ?Collision problem? - ?Collision response? - ?Collisions avoidance? - ?Cooperative environment? - ?Human robots? - ?Human-robot collaboration? - ?Human-robot-cooperation

Classification code: 214 Materials Science? - ?701.1 Electricity: Basic Concepts and Phenomena? - ?731.1 Control Systems? - ?731.5 Robotics? - ?732.1 Control Equipment? - ?914.1 Accidents and Accident Prevention? - ?1107 Human-Machine Systems

Numerical data indexing: Force -4.40E-01N, Force 1.20E+01N, Force 6.00E+00N, Percentage 1.309E+01%, Percentage 1.746E+01%, Percentage 2.10E+01%, Percentage 8.89E+00%, Percentage 8.90E+01%, Percentage 9.40E+01%, Size 4.30E-02m, Size 7.50E-02m, Size 9.40E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.069

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

15. Design and Experiment of High-efficiency Rotary Burial and Returning Machine for Uniform Broadcasting of Biochar

Accession number: 20253519069908

Title of translation: 生物炭均匀撒播高效旋埋还田机设计与试验

Authors: Tang, Han (1); Gu, Zejun (1); Xu, Ya’nan (1); Xu, Fudong (1); Wan, Chang (1); Wang, Jinwu (1)

Author affiliation: (1) College of Engineering, Northeast Agricultural University, Harbin; 150030, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 217-228

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the current challenges of biochar compaction, difficulty in broadcasting, and poor mixing and burial effects during field application, an agricultural practice of biochar broadcasting and rotary burial for field return was proposed. A high-efficiency rotary burial and uniform broadcasting machine for biochar was designed. The basic structure and working principles of the machine were described. Theoretical analyses were conducted on the biochar box stirring system, the forced biochar discharge system, and the rotary tillage system to determine key structural parameters. The main factors affecting the biochar discharge performance, such as motor speed and forward speed, were explored. Additionally, the main factors influencing biochar field return, such as the rotational speed of the rotary tillage blade roller, were investigated. Static bench tests based on motor speed were conducted by using discharge rate deviation, the coefficient of variation ( CV ) of discharge consistency among rows, and the CV of total discharge stability as test indicators to verify the broadcasting performance of the machine. Dynamic bench tests based on motor speed and forward speed were performed by using discharge rate deviation, the CV of discharge uniformity in the forward direction, and the CV of discharge uniformity in the working width direction as test indicators to evaluate the discharge performance and determine the effects of each factor on the test indicators. A two-factor, three-level orthogonal test was conducted to identify the optimal working parameters of the biochar broadcasting and rotary burial machine. Field tests were carried out to validate the bench test results. The results showed that under the optimal parameter combination of forward speed of 2. 5 km/h and motor speed of 75 r/min, the CV of discharge uniformity in the forward direction was 4. 23% , the CV of discharge uniformity in the working width direction was 5. 59% , the theoretical discharge rate was 5 019. 9 kg/hm2 , and the discharge rate deviation was 2. 34% . The overall performance of the machine met the agricultural requirements for biochar broadcasting and field return. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Soil conditioners

Controlled terms: Agricultural machinery? - ?Broadcasting? - ?Cultivation? - ?Soil testing? - ?Tillage

Uncontrolled terms: Bench tests? - ?Biochar? - ?Coefficients of variations? - ?Discharge rates? - ?Discharge uniformities? - ?Forward speed? - ?Higher efficiency? - ?Motor speed? - ?Rotary burial and returning machine? - ?Uniform broadcasting

Classification code: 483.1 Soils and Soil Mechanics? - ?716 Telecommunication; Radar, Radio and Television? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Angular velocity 1.2525E+00rad/s, Mass 9.00E+00kg, Percentage 2.30E+01%, Percentage 3.40E+01%, Percentage 5.90E+01%, Size 5.00E+03m

DOI: 10.6041/j.issn.1000-1298.2025.08.020

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

16. Driving Mechanism and Ecological Regulation Effect of Water-saving Irrigation on Degradation of Polycyclic Aromatic Hydrocarbons in Rice-Crab Co-cultivation

Accession number: 20253519059772

Title of translation: 节水灌溉对稻蟹共作多环芳烃降解的驱动机制及其生态调控效应

Authors: Sun, Nan (1); Wang, Siming (1); Yang, Anpei (2); Wang, Xuebing (1); Yan, Hao (1)

Author affiliation: (1) College of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin; 150030, China; (2) China Water Northeast Survey, Design and Research Co., Ltd., Changchun; 130042, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 578-588

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The driving mechanism and ecological regulation effect of water-saving irrigation on the degradation of polycyclic aromatic hydrocarbons (PAHs) in rice — crab co-cultivation system was revealed. By comparing the conventional submerged irrigation (CF), shallow and wet irrigation (SWD) and alternate dry and wet irrigation (AWD) modes, it was found that the degradation efficiency of soil phenanthrene was significantly increased by 95. 36% and 82. 99% under water-saving irrigation (AWD \  SWD) treatments, which was 43. 01% and 24. 46% higher than that of CF (66. 68%), respectively. AWD treatment was more excellent in reducing the phenanthrene bioaccumulation of rice crab system, significantly reducing the accumulation of PAHs in roots, stems and leaves by 35% ~ 55%, and the enrichment of phenanthrene in crab body was decreased by 52.08% compared with that of CF, systematically blocking the risk of contaminant food chain transmission. Furthermore, water-saving irrigation (AWD \  SWD) effectively enhanced soil environmental quality, which was achieved through reshaping soil aggregate structures and increasing oxygen availability, which promoted aerobic degradation of phenanthrene, activation of PAH metabolic decomposition pathways via improved redox enzymes and hydrolases, enrichment of soil nutrients that stimulated microbial community proliferation and structural optimization. The synergistic interactions of these factors significantly strengthened phenanthrene metabolism in rice — crab system soils. The research result elucidated the multi-interface collaborative degradation mechanism of phenanthrene driven by water-saving irrigation under rice — crab co-cropping mode, and provided an innovative paradigm for pollution control and resource collaborative optimization in ecological agriculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 40

Main heading: Degradation

Controlled terms: Abiotic? - ?Agribusiness? - ?Bioaccumulation? - ?Biochemistry? - ?Cultivation? - ?Irrigation? - ?Metabolism? - ?Plants (botany)? - ?Pollution control? - ?Polycyclic aromatic hydrocarbons ? - ?Shellfish? - ?Soil pollution? - ?Structural optimization? - ?Underwater soils? - ?Water conservation? - ?Water pollution

Uncontrolled terms: Blockings? - ?Co-cultivation? - ?Degradation efficiency? - ?Driving mechanism? - ?Dry and wet? - ?Effect of water? - ?Polycyclic aromatics? - ?Rice — crab co-cultivation? - ?Under water? - ?Water-saving irrigation

Classification code: 103 Biology? - ?444 Water Resources? - ?471.3 Oceanographic Techniques? - ?483.1 Soils and Soil Mechanics? - ?801.1 Biochemistry? - ?802.2 Chemical Reactions? - ?804.1 Organic Compounds? - ?821.4 Agricultural Methods? - ?1201.7 Optimization Techniques? - ?1501.2.1 Resource Conservation? - ?1502.1.1 Pollution? - ?1502.1.1.2 Water Pollution? - ?1502.1.1.3 Soil Pollution? - ?1502.1.1.4 Pollution Control? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 1.00E00%, Percentage 3.50E+01%, Percentage 3.60E+01%, Percentage 4.60E+01%, Percentage 5.208E+01%, Percentage 5.50E+01%, Percentage 6.80E+01%, Percentage 9.90E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.055

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

17. Method for Detection and Localization of Mulberry Leaf Harvesting and Branch Pruning Based on Improved YOLO v5

Accession number: 20253519059766

Title of translation: 基于改进 YOLO v5 的桑叶采摘与桑枝伐条识别定位方法

Authors: Shen, Yanqing (1, 2); Li, Li (1, 2); Li, Yuanming (4); Tong, Xiaoling (3); Zhou, Yongzhong (4)

Author affiliation: (1) College of Engineering and Technology, Southwest University, Chongqing; 400715, China; (2) Yibin Academy of Southwest University, Yibin; 644000, China; (3) State Key Laboratory of Resource Insects, Chongqing; 400715, China; (4) Chongqing Sunfeel Intelligent Technology Co., Ltd., Chongqing; 401121, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 487-495

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the multi-seasonal growth and morphological diversity of mulberry trees, and meet the demands of synchronizing with the silkworm breeding cycle, a dataset encompassing various climatic conditions and mulberry branch morphologies in July 2021, September 2024, and November 2024 was established. An improved YOLO v5-based mulberry branch detection model, YOLO v5 — cytp was proposed, and a 3D positioning system was developed by using a depth camera to achieve precise identification. Firstly, the CA attention mechanism was incorporated to enhance the model’s feature extraction capability for the lower parts of mulberry branches. Secondly, the original CIoU loss function in YOLO v5 was replaced with the SIoU loss function to improve training speed and inference accuracy. Finally, the lightweight GhostNet was adopted to reconstruct the backbone network of YOLO v5, ensuring the models performance while reducing its size. The calibration of the depth camera was completed, alignment between RGB images and depth images was achieved, and the target 3D coordinates were obtained through coordinate transformation. Experimental results showed that the YOLO v5 — cytp model achieved an average precision of 93.4%, representing a 1.2 percentage points improvement over the original YOLO v5 model. Meanwhile, the memory footprint was reduced from 3. 79 MB to 3. 02 MB, with a reduction of 20. 31% . The identification rate of the model for mulberry branches reached 91. 11%, and the maximum errors in the 3D coordinate positioning of the lower part of the branches (X, Y, Z) were (11. 3, 14. 1, 27. 0)mm, respectively, which were within the allowable error range. The research result can simultaneously realize the identification and positioning of mulberry leaf picking from bottom to top and branch pruning operations, providing a reference for the development of intelligent mulberry harvesting and pruning robots. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 20

Main heading: Object detection

Controlled terms: Cameras? - ?Forestry? - ?Plants (botany)? - ?Trees (mathematics)

Uncontrolled terms: 3D coordinates? - ?Branch pruning? - ?Depth camera? - ?Detection and localization? - ?Loss functions? - ?Mulberry branch pruning? - ?Mulberry leaf harvesting? - ?Objects detection? - ?Seasonal growth? - ?YOLO v5

Classification code: 103 Biology? - ?742.2 Photographic and Video Equipment? - ?821.1 Woodlands and Forestry? - ?1106.8 Computer Vision? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory

Numerical data indexing: Percentage 1.10E+01%, Percentage 3.10E+01%, Percentage 9.34E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.046

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

18. Error Modeling and Parameter Identification of Parallel Robots Based on Modified Rime Optimization Algorithm

Accession number: 20253519059788

Title of translation: 基于改进霜冰算法的并联机器人误差建模与参数辨识

Authors: San, Hongjun (1, 2); Zhang, Haobin (1); Chen, Jiupeng (1, 2); Wu, Xingmei (1); Wang, Ziyan (1); Chen, Wanlei (1)

Author affiliation: (1) Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming; 650500, China; (2) Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming; 650500, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 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: Aiming at the problems of complex error model and low efficiency of parameter identification in the kinematic calibration of parallel robots, an error modeling method and an efficient, stable parameter identification algorithm were proposed for parallel robots. Kinematic analysis of the parallel robot was completed based on the closed-loop vector method. Building upon this foundation, the concept of equivalent errors was introduced, and a corresponding error model was established. To meet the high-precision requirements of parameter identification algorithms, improvements were made to overcome the low convergence accuracy deficiency of the rime optimization algorithm (RIME). Specifically, the bisection method, Levy selection operator, and alternating sine-cosine strategy were proposed to enhance its initialization performance, global optimization capability, and local optimization capability. The improved algorithm, termed modified RIME (MRIME), was then employed for error parameter identification. Based on the identification results, compensation was applied to the robot’s drive inputs. Calibration experiments were conducted by using a Delta robot as the study object. Experimental results demonstrated that the modified RIME algorithm significantly improved optimization efficiency, accuracy, and stability. The average parameter identification time was 0. 126 s. After calibration, the robot’s average positional accuracy was improved by 41. 96% . These results validated the effectiveness of the proposed error model and parameter identification algorithm. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Parameter estimation

Controlled terms: Calibration? - ?Errors? - ?Global optimization? - ?Kinematics? - ?Robots

Uncontrolled terms: Equivalent error? - ?Error model identifications? - ?Error modeling? - ?Error parameters? - ?Model and parameters identification? - ?Modified RIME algorithm? - ?Optimization algorithms? - ?Parallel robots? - ?Parameter identification algorithms? - ?Parameters identification

Classification code: 731.1.1 Error Handling? - ?731.5 Robotics? - ?1201 Mathematics? - ?1201.7 Optimization Techniques? - ?1202 Statistical Methods? - ?1301.1.1 Mechanics

Numerical data indexing: Percentage 9.60E+01%, Time 1.26E+02s

DOI: 10.6041/j.issn.1000-1298.2025.08.067

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

19. Design and Experiment of Subsoil-harrow Depth Hydraulic Active Adjustable Device of Compound Land Preparation Machine

Accession number: 20253519069890

Title of translation: 复式整地机松耙深度液压主动调节装置设计与试验

Authors: Qiu, Baoci (1, 2); He, Jin (1, 2); Wu, Guanyu (1, 2); Qiu, Mei (1, 2); Wang, Quanyu (1, 3); Wang, Chao (1, 2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Equipment for Conservation Tillage, Ministry of Agriculture and Rural Affairs, Beijing; 100083, China; (3) Scientific Observing and Experiment Station of Arable Land Conservation (North Hebei), Ministry of Agriculture and Rural Affairs, 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: 8

Issue date: 2025

Publication year: 2025

Pages: 207-216 and 251

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problem that the adjustment range of subsoiling depth and harrowing depth of existing compound ground preparation machine is limited, a compound land preparation machine with subsoil-harrow depth hydraulic active adjustable device was designed. Through the hydraulic driving mechanism and parallelogram connecting rod structure, the structural parameters of the subsoil depth adjusting device and the harrow depth adjusting device were designed, and the subsoil depth and harrow depth can be adjusted independently. The working force state of the subsoiler group and the rake group was systematically analyzed, and the maximum traction force of the two groups was clarified. The matching design of the subsoiling-harrowing depth active adjustable device was carried out, and the structure analysis of the main frame was carried out to ensure that it can meet the operation requirements. The electro-hydraulic control system was designed to realize the acquisition and control of each operating parameter based on sensor monitoring. The dynamic force analysis of the depth adjusting hydraulic cylinder was carried out, and the maximum load of the subsoil adjusting hydraulic cylinder was determined to be 12. 3 kN , and the maximum load of the harrow depth adjusting hydraulic cylinder was 4.5 kN. The model parameters of the hydraulic cylinder were clarified, among which the cylinder diameter of the subsoil adjusting hydraulic cylinder was 63 mm, and the piston rod diameter was 35 mm. The cylinder diameter of the harrow depth adjusting hydraulic cylinder was 50 mm, and the piston rod diameter was 28 mm. The results of the field experiment showed that the subsoiling depth was 300 ~ 461. 6 mm, and the harrowing depth was 120 ~ 157. 4 mm. The stability of the depth of the machine and tool was 97. 50% , and the stability coefficient of the rake depth was 96. 03% . The rate of broken soil was 76. 9% , and the standard deviation of ground flatness was 0. 4 cm. All indicators met the design requirements and national industry standards. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Pistons

Controlled terms: Agricultural machinery? - ?Cylinders (shapes)? - ?Hydraulic machinery? - ?Structural design? - ?Transmissions

Uncontrolled terms: Active adjustments? - ?Compound land preparation machine? - ?Cylinder diameters? - ?Electrohydraulic controls? - ?Harrowing? - ?Hydraulic cylinders? - ?Hydraulic driving mechanism? - ?Maximum load? - ?Piston rod? - ?Subsoiling

Classification code: 408 Structural Design? - ?408.1 Structural Members and Shapes? - ?602.2 Mechanical Transmissions? - ?608.1.1 Internal Combustion Engine Components? - ?821.2 Agricultural Machinery and Equipment? - ?1401.2 Hydraulic Equipment and Machinery

Numerical data indexing: Force 3.00E+03N, Force 4.50E+03N, Percentage 3.00E+00%, Percentage 5.00E+01%, Percentage 9.00E+00%, Size 2.80E-02m, Size 3.50E-02m, Size 4.00E-02m, Size 4.00E-03m, Size 5.00E-02m, Size 6.00E-03m, Size 6.30E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.019

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

20. Temporal and Spatial Dynamics Evolution of Surface Soil Moisture Content in Agricultural Fields During Crop Growth Period Based on TVDI

Accession number: 20253519059778

Title of translation: 基于 TVDI 的作物生育期农田表层土壤含水率时空动态演变

Authors: Miao, Ze (1); Qu, Zhongyi (1, 2); Bai, Yanying (1); Liu, Quanming (1); Wang, Liping (1); Liu, Qi (1)

Author affiliation: (1) College of Water Conservation and Civil Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China; (2) School oj Energy and Environment, Inner Mongolia University of Science and Technology, Baotou; 014010, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 567-577

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The issues of low agricultural water use efficiency and insufficient refined water resource management in the Hetao Irrigation District were addressed by constructing the temperature vegetation dryness index (TVDI) using multi-temporal Landsat — 8/9 data. The characteristics of land surface temperature (LST)— vegetation index (normalized difference vegetation index (NDVI)) eigenspace and dry and wet side change characteristics of different vegetation covers were analyzed. Combined with the measured soil moisture content data, the accuracy and applicability of the TVDI model for the inversion of soil moisture content in farmland during the crop reproductive period were assessed, and the spatial and temporal variations of the surface soil moisture distribution in farmland from May to September in 2022 and 2023 were investigated, respectively. The results showed that the temperature maxima that deviated from the trend should be excluded when linearly fit the wet and dry edges, and the wet and dry edge equations were obtained by linear fitting in the interval of NDVI of 0. 2 ~ 0. 8, with the coefficients of determination of the dry edge equations of R not less than 0. 85, and those of the wet edge equations of R ranged from 0. 21 to 0. 96. The slopes of the wet edges varied with a cyclic cosine wave law in both 2022 and 2023. The dry edge slopes were all negative, and the dry edge slopes varied from large to small to large with the mean TVDI value in 2022 showing that the slope changed from steep to gentle and then steep again, and the dry edge slopes changed insignificantly in 2023. The dry and wet edge intercepts increased and then decreased, which was consistent with the trend of surface temperature change. Soil moisture varied significantly, decreasing from late May to mid-June, increasing from late June, being the highest in August and the lowest in late September. The spatial distribution of soil moisture was significantly affected by irrigation and precipitation, and the TVDI model can effectively reflect the spatial and temporal changes of soil moisture at the regional scale, which can provide a scientific basis for improving the efficiency of agricultural water use and formulating refined irrigation strategies. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 40

Main heading: Soil moisture

Controlled terms: Atmospheric temperature? - ?Efficiency? - ?Farms? - ?Irrigation? - ?Land surface temperature? - ?Moisture determination? - ?Soil surveys? - ?Soil temperature? - ?Spatial distribution? - ?Surface measurement ? - ?Surface temperature? - ?Vegetation? - ?Water resources

Uncontrolled terms: Agricultural field area? - ?Agricultural fields? - ?Feature space? - ?Land surface temperature? - ?Land surface temperature-normalized difference vegetation index feature space? - ?LANDSAT? - ?Landsat-8/9? - ?Normalized difference vegetation index? - ?Soil moisture content? - ?Spatial ? - ?Temperature-vegetation dryness indices? - ?Temporal change

Classification code: 103 Biology? - ?208 Coatings, Surfaces, Finishes, Films and Deposition? - ?302.2 Heat Transfer? - ?405.3 Surveying? - ?443 Meteorology? - ?443.1 Atmospheric Properties? - ?444 Water Resources? - ?483.1 Soils and Soil Mechanics? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.4 Agricultural Methods? - ?913.1 Production Engineering? - ?941.5 Mechanical Variables Measurements? - ?941.6 Moisture Measurements? - ?1201 Mathematics

DOI: 10.6041/j.issn.1000-1298.2025.08.054

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

21. Meteorological Drought Monitoring Model Based on Multi-source Data and Stacking Ensemble Learning

Accession number: 20253519069891

Title of translation: 基于多源数据和Stacking集成学习的气象干旱监测模型

Authors: Liu, Hangcheng (1); Yao, Ning (1); Yu, Xuchuang (1); Xiangli, Jiangfeng (2); Huang, Xifeng (2); Li, Yongmin (2)

Author affiliation: (1) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Shaanxi Provincial Flood and Drought Disaster Prevention Center, Xi’an; 710004, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 107-119

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: As a complex natural disaster exhibiting marked spatiotemporal heterogeneity, drought threatens socio-economic systems and ecosystem resilience through its frequent occurrence and cumulative destructive impacts. Therefore, accurate monitoring of drought events is of great practical significance. Focusing on Shaanxi Province as the research area and a comprehensive feature variable system was established by integrating vegetation, surface, and climate multi-dimensional drought factors. Using the optimal meteorological drought index as the target variable, the stacked ensemble drought index (SEDI) for Shaanxi Province during 2003-2020 was constructed based on Stacking ensemble learning and multiple machine learning algorithms, and its applicability in meteorological drought monitoring was evaluated. The results demonstrated that the monthly-scale variation trends of the meteorological drought composite index ( MCI ) , standardized precipitation index ( SPI ) , and standardized precipitation evapotranspiration index (SPEI) were generally consistent. However, MCI exhibited high accuracy and sensitivity in identifying drought events, thus it was selected as the target variable for the meteorological drought monitoring model. Among the three ensemble models and five single models, the ensemble model XGBjj , constructed based on XGBoost, demonstrated the best monitoring performance across different regions of Shaanxi Province, with coefficient of determination (R2) ranging from 0. 934 to 0. 945 and root mean square error (RMSE) ranging from 0. 208 to 0. 256. During 2003-2020, SEDI and MCI showed drought level matching rates of 87. 04% , 83. 80% , and 85. 65% at Yulin, Qindu, and Shiquan stations respectively, with highly consistent drought trends and simulated R2 values all exceeded 0. 91 , indicating SEDI’s effectiveness in identifying drought types and variation trends across different stations. Validation through two drought events (spring 2005 and summer 2015) confirmed SEDI’s strong applicability for regional-scale drought monitoring, exhibiting high consistency with MCI in spatial distribution characteristics and similarity in proportions of different drought severity levels, effectively reflecting spatiotemporal evolution patterns of drought processes. Spatial autocorrelation analysis demonstrated significant spatial clustering of meteorological drought in Shaanxi Province, with a global Moran s I of 0. 69 ( Z-score = 3. 58 , P ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 46

Main heading: Remote sensing

Controlled terms: Autocorrelation? - ?Disasters? - ?Drought? - ?Ecosystems? - ?Forestry? - ?Learning algorithms? - ?Learning systems? - ?Machine learning? - ?Mean square error? - ?Spatial variables measurement ? - ?Vegetation

Uncontrolled terms: Composite index? - ?Drought monitoring? - ?Ensemble learning? - ?Machine-learning? - ?Meteorological drought? - ?Remote-sensing? - ?Shaanxi province? - ?Spatial autocorrelations? - ?Stacking ensemble learning? - ?Stackings

Classification code: 103 Biology? - ?443.3 Precipitation? - ?444 Water Resources? - ?731.1 Control Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.1 Woodlands and Forestry? - ?914 Safety Engineering? - ?941.5 Mechanical Variables Measurements? - ?1101.2 Machine Learning? - ?1201 Mathematics? - ?1202.2 Mathematical Statistics? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 4.00E+00%, Percentage 6.50E+01%, Percentage 8.00E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.010

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

22. Optimization of Soil Moisture Machine Learning Inversion Model Considering Spatiotemporal Fusion of Environmental Factors

Accession number: 20253519072439

Title of translation: 考虑环境因素时空融合的土壤水分机器学习反演模型优化

Authors: Li, Ruiping (1, 2); Zhao, Jianwei (1); Wang, Fuqiang (1); Wang, Huan (1); Yu, Xin (3); Miao, Cunli (4)

Author affiliation: (1) College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China; (2) Collaborative Innovation Center of Inner Mongolia Autonomous Region for Comprehensive Management of Water Resources and Water Environment in the Inner Mongolia Section of the Yellow River Basin, Hohhot; 010018, China; (3) Inner Mongolia Autonomous Region Surveying and Mapping Geographic Information Center, Hohhot; 010010, China; (4) Natural Resources Bureau of Hexigten Banner, Hexigten, 025350, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 370-379

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: As one of the key elements to construct soil moisture inversion model, vegetation index mainly comes from the extraction of remote sensing images. In view of the shortcomings that high spatiotemporal resolution images are difficult to obtain, the adaptive spatiotemporal fusion model (OL - STARFM) with object-level processing strategy was used to fuse the remote sensing images in the study area, and the normalized difference vegetation index (NDVI), land surface temperature (LST) and temperature vegetation dryness index (TVDI) were extracted as environmental variables, combined with land use type, soil texture, evapotranspiration, elevation, aspect, slope, original image vegetation drought index (TVDI), normalized vegetation index (NDVI), land surface temperature (LST), as well as temperature, precipitation and wind speed as modeling factors, a soil moisture inversion model based on three methods, namely multiple linear stepwise regression (MLSR), random forest (RF) and gradient booster (GBM), was constructed and optimized. The results showed that land surface temperature was the key influencing factor affecting the spatial variability of soil moisture (R was - 0. 46), followed by evapotranspiration (-0.43), air temperature (-0.39), F_NDVI (0.38), NDVI (0.36), land use type (0. 31), F_TVDI (-0. 3), TVDI (- 0. 28), precipitation (0. 27), soil texture (0. 27), slope aspect (- 0. 25), elevation (0. 26), slope (- 0. 20) and wind speed (- 0. 20). MLSR showed strong model linear processing ability. In the nonlinear processing, the RF regression model was the most stable, and the GBM model had the highest accuracy, with R2 of 0. 910, and MAE, MSE and RMSE were 2. 12%, 6. 89% and 2. 62%, respectively. The prediction accuracy of the multiple stepwise regression method in the process of soil moisture inversion was low, which showed the limitations of the linear model in dealing with complex relationships. The correlation coefficients between TVDI and NDVI extracted by the OL - STARFM fusion method and soil moisture were -0. 41 and 0. 38, respectively, which were higher than the correlation between vegetation index and soil moisture extracted from a single image, and effectively improved the accuracy of the soil moisture inversion model, indicating the feasibility of the method in the construction of soil moisture inversion model, and providing a theoretical basis for obtaining continuous high spatiotemporal resolution images for effective continuous monitoring of soil moisture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Soil moisture

Controlled terms: Atmospheric temperature? - ?Gradient methods? - ?Land surface temperature? - ?Land use? - ?Learning systems? - ?Machine learning? - ?Moisture determination? - ?Random forests? - ?Regression analysis? - ?Soil surveys ? - ?Soil temperature? - ?Surface measurement? - ?Surface properties? - ?Tantalum compounds? - ?Textures? - ?Vegetation? - ?Wind

Uncontrolled terms: %moisture? - ?Environmental factors? - ?Inversion models? - ?Machine learning algorithms? - ?OL - STARFM? - ?Remote sensing inversion model? - ?Remote-sensing? - ?Spatial temporals? - ?Spatial-temporal fusion? - ?Temperature-vegetation dryness indices

Classification code: 103 Biology? - ?202.3 Chromium, Manganese, Molybdenum, Tantalum, Tungsten, Vanadium and Alloys? - ?208 Coatings, Surfaces, Finishes, Films and Deposition? - ?214 Materials Science? - ?403 Urban and Regional Planning and Development? - ?405.3 Surveying? - ?443 Meteorology? - ?443.1 Atmospheric Properties? - ?483.1 Soils and Soil Mechanics? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?941.5 Mechanical Variables Measurements? - ?941.6 Moisture Measurements? - ?1101.2 Machine Learning? - ?1201.7 Optimization Techniques? - ?1202.2 Mathematical Statistics? - ?1301.1.2 Physical Properties of Gases, Liquids and Solids

Numerical data indexing: Percentage 1.20E+01%, Percentage 6.20E+01%, Percentage 8.90E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.034

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

23. Data Fusion-based High Spatiotemporal Resolution Retrieval of Crop Evapotranspiration and Efficient Refined Irrigation Decision-making for Irrigation Areas

Accession number: 20253519069932

Title of translation: 基于数据融合的高时空分辨率作物蒸散发反演与高效精细化灌溉决策

Authors: Li, Mo (1, 2); Xu, Min (1); Wang, Luchen (1, 2); Wang, Yijia (1, 2); Dong, Wenhao (1)

Author affiliation: (1) School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin; 150030, China; (2) National Key Laboratory of Smart Farm Technology and System, Harbin; 150030, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 62-73

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Efficient water resources utilization and precision irrigation management are critical for improving agricultural productivity. Evapotranspiration (ET) estimation, a pivotal parameter in irrigation water management, has traditionally been limited by low spatiotemporal resolution, thereby constraining the implementation of precise irrigation practices. A framework combining remote sensing, data fusion, and multi-objective optimization for high-resolution evapotranspiration ( ET ) estimation and irrigation management was presented. The framework used a spatiotemporal fusion model to generate accurate surface variables ( NDVI, Albedo, land surface temperature) , enabling daily field-scale (30 m X 30 m) ET estimation. It also integrated the non-dominated sorting genetic algorithm II ( NSGA - II) to optimize irrigation strategies for different districts. Results showed that the framework effectively addressed spatiotemporal variability, providing precise irrigation to meet crop water needs. Optimized irrigation reduced water use by 57 mm during non-critical growth stages, increased crop yield by 423.23 kg/hm2 , achieving both water savings and yield enhancement. Analysis with the temperature vegetation drought index (TVDI) revealed spatial differences; in humid zones (0 2 ) while minimizing water waste. In arid zones (TVDI >0. 4) , where insufficient irrigation reduced yields (7 500 ~ 8 500 kg/hm2) , increased irrigation by 37. 15 mm, boosted yields by 2 171.88 kg/hm2 , alleviating drought risks. Overall, the framework improved agricultural water management, increased regional yield by 4.6% , enhanced irrigation efficiency by 14% , and reduced irrigation volume by 11%. This decision-making framework at a 30 m grid scale offered valuable insights for sustainable precision agriculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Evapotranspiration

Controlled terms: Agribusiness? - ?Arid regions? - ?Crops? - ?Data fusion? - ?Decision making? - ?Drought? - ?Genetic algorithms? - ?Irrigation? - ?Multiobjective optimization? - ?Precision agriculture ? - ?Surface measurement? - ?Water conservation? - ?Water management

Uncontrolled terms: Crop evapotranspiration? - ?Drought monitoring? - ?Irrigation management? - ?Irrigation scheduling? - ?Irrigation scheduling optimization? - ?Management IS? - ?Remote-sensing? - ?Scheduling optimization? - ?Spatio-temporal resolution? - ?Vegetation drought indices

Classification code: 443 Meteorology? - ?443.3 Precipitation? - ?444 Water Resources? - ?731.1 Control Systems? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?912.2 Management? - ?941.5 Mechanical Variables Measurements? - ?1106 Computer Software, Data Handling and Applications? - ?1106.2 Data Handling and Data Processing? - ?1201.7 Optimization Techniques? - ?1501.2.1 Resource Conservation? - ?1502.3 Hydrology

Numerical data indexing: Mass 1.7188E+02kg, Mass 4.2323E+02kg, Mass 5.00E+02kg, Percentage 1.10E+01%, Percentage 1.40E+01%, Percentage 4.60E+00%, Size 1.50E-02m, Size 1.70E-02m, Size 3.00E+01m, Size 5.70E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.006

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

24. Design and Experiment of Bionic Mole Forelimb Adaptive Stubble Removal Device

Accession number: 20253519072717

Title of translation: 仿生鼹鼠前肢自适应除茬装置的设计与实验

Authors: Li, Mingwei (1, 2); Guo, Caiyun (1); Chen, Yulong (1, 2); Zhou, Long (1, 2); Xia, Xiaomeng (1, 3); Wang, Wenjun (1, 2)

Author affiliation: (1) College of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo; 255000, China; (2) Institute of Modern Agricultural Equipment, Shandong University of Technology, Zibo; 255000, China; (3) Shandong Key Laboratory of Smart Agricultural Technology and Intelligent Agricultural Machinery and Equipment for Field Crops, Zibo; 255000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 320-332 and 389

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Due to the adoption of no-till sowing technology, which results in thick and abundant corn stalks, traditional stalk cleaning mechanisms lack sufficient throwing force and exhibit unstable operating depths, severely constraining seeding quality. A bionic mole forelimb adaptive stubble removal device was designed, which can effectively remove stubble under full straw mulch. The stalk cleaning device consisted of a bionic stalk cleaning mechanism and an adaptive cleaning control system. The operational mechanism of the bionic stalk cleaning device, incorporating the movement morphology and law structure inspired by mole forelimbs, was investigated through theoretical analysis and computer simulation, aiming to elucidate the influence of structural parameters n the quality of cleaning. A fuzzy control algorithm was used to design the adaptive cleaning control system, enhancing the stability of the cleaning device’s operating depth. The structure design and parameters of bionic straw removal mechanism were optimized by EDEM simulation software, and the type of bionic straw removal mechanism was determined. The regression equation between different parameters of the bionic straw removal mechanism and the evaluation index was established by Design-Expert software, and the optimal parameter combination was determined; the rotation radius was 150 mm, the bionic angle was 17.71°, the straw cleaning rate was 92. 30%, and the working resistance was 30. 38 N. Field experiments were conducted to verify the operational performance of the bionic mole forelimb stalk cleaning mechanism and the adaptive cleaning control system, comparing them with a conventional flat stalk cleaning mechanism. The experimental results demonstrated that the performance of the bionic stalk cleaning mechanism exceeded that of the conventional flat stalk cleaning mechanism, and the adaptive cleaning control system effectively improved the performance of the cleaning mechanism. When operating in fields with full straw coverage, the clearance rate of the bionic mole forelimb-adaptive stalk cleaning device reached 90. 9%, which was 21.3 percentage points higher than that of the conventional stalk cleaning mechanism, meeting the agricultural requirements for no-tillage corn seeding after operation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Adaptive control systems

Controlled terms: Agricultural machinery? - ?Bionics? - ?Cleaning? - ?Computer software? - ?Quality control? - ?Seed? - ?System stability

Uncontrolled terms: Adaptive Control? - ?Bionic stalk cleaning mechanism? - ?Cleaning devices? - ?Corn stalk? - ?Mole forelimb? - ?No-till sowing? - ?Performance? - ?Removal mechanism? - ?Seeding qualities? - ?Straw mulch

Classification code: 101.1 Biomedical Engineering? - ?731.1 Control Systems? - ?802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?913.3 Quality Assurance and Control? - ?961 Systems Science? - ?1106.9 Computer Software

Numerical data indexing: Force 3.80E+01N, Percentage 3.00E+01%, Percentage 9.00E+00%, Size 1.50E-01m

DOI: 10.6041/j.issn.1000-1298.2025.08.030

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

25. Multi-source Image Fusion Recognition of Crawling Bank Grass Crayfish Based on FAS-YOLOv8n

Accession number: 20253519059780

Title of translation: 基于 FAS-YOLOv8n 的爬岸上草小龙虾多源图像融合识别方法

Authors: Li, Lu (1, 2); Sun, Chaoqi (1); Zhou, Yufan (1); Zhou, Chengyu (1); Kou, Shengzhou (1); Chen, Yanqi (1)

Author affiliation: (1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Aquaculture Facilities Engineering, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 526-534

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: To address the issues of low efficiency and high labor costs associated with nighttime inspections in crayfish farming, a lightweight method for identifying crayfish on the bank, termed FAS — Y0L0v8n, was proposed based on an improved YOLO v8n architecture. Firstly, to tackle the problem of poor image quality of crayfish captured at night, both RGB and infrared images were collected to integrate multi-source information. Secondly, Ghost convolutions and C2f_Repghost modules were employed in the backbone network of YOLO v8n to reduce the model’s parameter count. Additionally, a deformable attention (DA) mechanism was introduced between the backbone and neck networks to enhance the model’s focus on crayfish and improve feature extraction efficiency. Finally, the VoVGSCSP module replaced the C2f module to accelerate feature fusion in the neck network, further reducing computational load. Experimental results indicated that the improved FAS — YOLOv8n model achieved a recognition accuracy of 90. 62% on the integrated image dataset, with mean average precision (mAP) of 92. 9% and recall rate of 85%. Compared with the RGB and infrared image datasets, the recognition accuracy, mAP, and recall rate was improved by 6. 05 and 8. 46, 4. 78 and 7. 14, and 3. 84 and 3. 87 percentage points, respectively. When tested on the integrated dataset, the improved FAS — YOLOv8n model demonstrated a 5. 1 percentage points increase in mAP over the original model, while reducing parameter count and computational load by 13. 29% and 23. 17%, respectively. The model size was only 6. 2 MB, with detection speed of 86 frames per second. Its recognition performance surpassed that of other mainstream object detection models, enabling lightweight deployment and providing technical support for the application of drones in pond inspections. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 21

Main heading: Image fusion

Controlled terms: Deep learning? - ?Image enhancement? - ?Image quality? - ?Infrared imaging? - ?Object detection? - ?Wages

Uncontrolled terms: Computational loads? - ?Crayfish? - ?Deep learning? - ?Image datasets? - ?Multi-source images? - ?Objects recognition? - ?Percentage points? - ?Recall rate? - ?Recognition accuracy? - ?YOLO v8n

Classification code: 746 Imaging Techniques? - ?912.3 Personnel? - ?1101.2.1 Deep Learning? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 1.70E+01%, Percentage 2.90E+01%, Percentage 6.20E+01%, Percentage 8.50E+01%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.08.050

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

26. Prediction of Soil Moisture Content Based on Transformer and Multi Sequence Feature

Accession number: 20253519069937

Title of translation: 基于Transformer和多序列特征的土壤含水率预测

Authors: Kuang, Xiaofei (1); Wan, Liping (1); Lian, Jiaqian (1); Duan, Xinyue (1); Wei, Pengliang (1); Guo, Jiao (1, 2)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Shaanxi Key Laboratory of Agriculture Information Perception and Intelligent Service, Shaanxi, Yangling; 712100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 120-127

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate prediction of soil moisture content ( SMC ) is very important in agricultural production. Multi time series and multi-source remote sensing data can provide time change information with multiple characteristics, but multi temporal and multi sequence information is often not effectively used in SMC inversion. We hope to use multiple time series features to predict the change trend of SMC. Transformer network performed well in processing multiple sequence features. A deep regression model for SMC extraction was constructed based on Transformer structure, and it was compared with convolutional neural network regression (CNNR) , long short-term memory ( LSTM ) regression, and gated current unit (GRU) regression. Multi source heterogeneous remote sensing data, including Sentinel 1, soil moisture active passive (SMAP) , etc. were used as model inputs, and field measurement data were used as SMC reference values. The experimental results showed that the use of long time series feature data was more conducive to SMC prediction. When using the historical data of 5 days to predict SMC after 5 days, compared with CNNR, LSTM and GRU, the determination coefficient of Transformer regression was increased by 0. 095 3 , 0. 032 4 and 0. 033 6 on average, and the root mean square error was decreased by 0. 014 cm3 /cm3 , 0. 002 6 cm3 /cm3 and 0. 003 0 cm3 /cm3 on average. The feature extraction and regression mechanism of the model were analyzed by quantifying the impact of input features on regression, the sequence changes of hidden features in the middle, and the output performance. The analysis of feature influence and the change of hidden features in the middle showed that allocating appropriate attention to features at different times was more conducive to predicting SMC. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Soil moisture

Controlled terms: Data mining? - ?Extraction? - ?Forecasting? - ?Mean square error? - ?Regression analysis? - ?Soil surveys? - ?Time series? - ?Time series analysis

Uncontrolled terms: Modeling analyzes? - ?Multi-sequences? - ?Multi-source remote sensing? - ?Multi-Sources? - ?Multiple time series? - ?Remote sensing data? - ?Remote-sensing? - ?Sequence features? - ?Soil moisture content? - ?Transformer

Classification code: 405.3 Surveying? - ?483.1 Soils and Soil Mechanics? - ?802.3 Chemical Operations? - ?1106.2.1 Data Mining? - ?1202 Statistical Methods? - ?1202.2 Mathematical Statistics

Numerical data indexing: Age 1.37E-02yr, Size 0.00E00m, Size 1.40E-01m, Size 6.00E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.011

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

27. Virtual Collecting Method of Ambient Temperature in Large-scale Breeding Chicken House

Accession number: 20253519059771

Title of translation: 规模化养殖鸡舍环境温度虚拟采集方法研究

Authors: Jia, Yuchen (1, 2); Fu, Annan (3); Li, Lihua (3, 4); Hu, Changzeng (3); Huo, Limin (2, 3)

Author affiliation: (1) College of Information Technology, Hebei Agricultural University, Baoding; 071000, China; (2) Key Laboratory of Intelligent Equipment and New Energy Utilization in Livestock and Poultry E arming of Hebei Province, Baoding; 071000, China; (3) College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding; 071000, China; (4) Key Laboratory of Facilities for Poultry and Egg Production Engineering, Ministry of Agriculture and Rural Affairs, Baoding; 071000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 644-654

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: At present, under the background of the highly intensive development of livestock and poultry breeding, the intelligent control of the number of poultry houses is particularly important. The contradiction between the construction of data model supported by massive information collection and the limited number of physical nodes of the Internet of Things is a problem currently facing. A virtual temperature acquisition method combining current reference point and historical data was proposed. Firstly, computational fluid dynamics (CFD) simulation was used to analyze and determine the temperature distribution and environmental characteristics inside the chicken house, and the collection area was preliminarily divided according to the CFD simulation results. Then combined with gray correlation degree and cosine similarity analysis, the reference points that were highly correlated with temperature of key unmonitored area were effectively identified. Finally, artificial intelligence algorithms such as XGBoost and WOA — BiLSTM were used to predict temperatures in areas not directly monitored. Through the test in a laying chicken farm in Xingtai City, Hebei Province, the average absolute error between the data of ten virtual collection points and the actual data was within 0. 25 T!, which ensured the reliability of the data and provided enough data for the intelligent control modeling of the number of poultry houses, and provided an important technical basis for the practice of smart agriculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Computational fluid dynamics

Controlled terms: Agriculture? - ?Animals? - ?Artificial intelligence? - ?Data acquisition? - ?Data reliability? - ?Intelligent control? - ?Livestock? - ?Optimization? - ?Smart agriculture? - ?Virtual reality

Uncontrolled terms: Ambients? - ?BiLSTM model? - ?Chicken house? - ?Composite similarity index? - ?Optimization algorithms? - ?Poultry house? - ?Reference points? - ?Similarity indices? - ?Virtual acquisition? - ?Whale optimization algorithm

Classification code: 103 Biology? - ?301.1.4 Computational Fluid Dynamics? - ?731.1 Control Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?1101 Artificial Intelligence? - ?1106.2 Data Handling and Data Processing? - ?1107.1 Virtual Reality Technology? - ?1201.7 Optimization Techniques

Numerical data indexing: Magnetic flux density 2.50E+01T

DOI: 10.6041/j.issn.1000-1298.2025.08.061

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

28. Research Progress and Prospects of Mechanical Technology for Whole Machine of High-quality Tea Picking Robot

Accession number: 20253519069902

Title of translation: 名优茶采摘机器人整机机械技术研究进展与展望

Authors: Jia, Jiangming (1, 2); Wang, Dengyi (1); He, Leiying (1, 2); Wu, Chuanyu (2, 3); Chen, Jianneng (1, 2); Zhang, Jianyi (1); Li, Yatao (1, 2)

Author affiliation: (1) School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou; 310018, China; (2) Key Laboratory of Agricultural Intelligent Perception and Robotics of Zhejiang Province, Hangzhou; 310018, China; (3) Zhejiang Ocean University, Zhoushan; 316022, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 193-206

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The picking of high-quality tea is the most labor-intensive process in tea planting and production. With the shortage of labor and the rising cost of labor, it has seriously constrained the sustainable and healthy development of the high-quality tea industry. Replacing manual picking operations with agricultural picking robots has become an important trend in the development of modern agriculture. High-quality tea is a unique tea category in China, and research on high-quality tea picking robot worldwide was mainly carried out in domestic research institutes. Firstly, by analyzing existing domestic and foreign literatures, the research focused on key mechanical technologies such as the picking end effector, picking robotic arm, and motion chassis of the high-quality tea picking robot. The technical solutions of the key technologies were compared and analyzed, and the application characteristics and existing problems of the technical solutions were summarized. Secondly, three development stages were divided for the research of the whole machine of the high-quality tea picking robot. Finally, the key mechanical technical application requirements and technical challenges of the whole machine of the high-quality tea picking robot were elaborated, and the future technological development trends of the key mechanical technologies of the whole machine of the high-quality tea picking robot were pointed out. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 85

Main heading: End effectors

Controlled terms: Agricultural robots? - ?Agriculture? - ?Employment? - ?Robotic arms? - ?Tea? - ?Wages

Uncontrolled terms: High quality? - ?High-quality tea? - ?Labor intensive process? - ?Mechanical technology? - ?Motion chassi? - ?Picking operations? - ?Picking robot? - ?Plantings? - ?Technical solutions? - ?Whole machine

Classification code: 101.6.1 Robotic Assistants? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?822.3 Food Products? - ?901 Engineering Profession? - ?912.3 Personnel

DOI: 10.6041/j.issn.1000-1298.2025.08.018

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

29. Automatic Extraction of Pig Body Size Parameters Based on Point Cloud

Accession number: 20253519059768

Title of translation: 基于点云的生猪体尺参数自动提取方法

Authors: Huang, Xia (1, 2); Yu, Songke (1); Liu, Xingming (3); Zhang, Bo (1, 2); Zhu, Fengbo (4)

Author affiliation: (1) School oj Electronic Engineering, Chengdu Technological University, Chengdu; 611730, China; (2) Special Robot Application Technology Research Institute, Chengdu Technological University, Chengdu; 611730, China; (3) Liaoning Metallurgical Ceological Exploration Research Institute Co., Ltd., Anshan; 114001, China; (4) School of Ceospatial Information, Information Engineering University, Zhengzhou; 450001, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 496-506

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The body dimension parameters of livestock play a crucial role in various fields such as animal selection breeding, health monitoring, and genetic studies of performance. The automatic measurement of these parameters represents an important research direction in digital agriculture. Relied on a dual-depth camera measurement system to collect point cloud data of live pigs and aimed to develop an algorithm capable of automatically identifying and extracting pig feature points, feature surfaces, and body dimension parameters. Given that pig point cloud data often contain significant noise issues, a filtering algorithm with multiple filters stacked was innovatively proposed, in contrast to traditional single-filter methods. To achieve automatic detection of pig feature points and surfaces, an algorithm for automatically extracting feature points was proposed based on the convexity and concavity of side and top-view contour lines, and an algorithm for automatically extracting feature surfaces based on the trend of trunk cross-sectional area changes, differing from approaches that calculated feature points from a single perspective. Addressing the limitations of existing body dimension parameters in terms of variety and fitting accuracy, totally 17 body dimension parameters and 12 common shape factors from pig point cloud data were successfully automatically extracted. To validate the effectiveness of the algorithm, a detailed analysis of 200 sets of point cloud data from 20 live pigs was conducted. Experimental results showed that the average total filtering error was 3. 84%, and under the condition that the original point cloud accuracy reached 0. 036 mm, the average measurement error of pig body dimension parameters was only 2. 46% . Additionally, a principal component-based weight analysis on the 17 geometric parameters and 12 shape factors of pigs was conducted, discussing the weights of different body dimension parameters and shape factors in pig geometric morphology analysis. In summary, the research result can provide an effective method for high-throughput measurement of animal three-dimensional phenotypic parameters, not only improving measurement accuracy and efficiency but also providing strong support for in-depth research in fields such as animal selection breeding and health monitoring. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 41

Main heading: Mammals

Controlled terms: Agriculture? - ?Anthropometry? - ?Batch data processing? - ?Data handling? - ?Factor analysis? - ?Geometry? - ?Livestock? - ?Mathematical morphology? - ?Morphology? - ?Parameter estimation ? - ?Principal component analysis? - ?Veterinary medicine

Uncontrolled terms: Batch processing of data? - ?Body dimensions? - ?Body sizes? - ?Health monitoring? - ?Measurements of? - ?Multiple filter superposition? - ?Pig? - ?Point cloud data? - ?Point-clouds? - ?Shapes factors

Classification code: 101.4 Biomechanics, Bionics and Biomimetics? - ?102.1 Medicine? - ?103 Biology? - ?214 Materials Science? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?1101.2 Machine Learning? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing? - ?1201 Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1201.14 Geometry and Topology? - ?1202 Statistical Methods? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 4.60E+01%, Percentage 8.40E+01%, Size 3.60E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.047

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

30. Detecting Method of Potato Seed Bud Eye Based on YOLO v8_EGW

Accession number: 20253519059785

Title of translation: 基于 YOLO v8_EGW 的马铃薯种薯芽眼检测方法

Authors: Huang, Hua (1); Zhang, Cundong (1); Zhang, Huiwang (1); Yue, Yun (2); Wang, Shijun (2); Wu, Yadong (1)

Author affiliation: (1) School of Mechanical and Electrical Engineering, Lanzhou University oj Technology, Lanzhou; 730050, China; (2) Gansu Academy of Agri-engineering Technology, 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: 8

Issue date: 2025

Publication year: 2025

Pages: 458-466 and 506

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to solve the problem that the traditional object detection algorithm is susceptible to the influence of soil covering, surface damage and environmental factors in potato sprout eye detection, resulting in poor detection effect, an YOLO v8_EGW model was proposed, which realized the fast and accurate detection of potato sprout eyes on the self-made potato data acquisition experimental bench. Firstly, in order to improve the ability to extract bud eye features on the potato surface, the EMA attention mechanism was introduced into the backbone part of YOLO v8. Secondly, the GD mechanism was introduced into the neck network of YOLO v8 and combined with the C2F module to strengthen the information fusion ability of bud eye features and improve the detection ability of bud eye features. Finally, the position loss function was replaced with the WIoU loss function to improve the quality of the marking box and further enhance the detection performancee. The results showed that the precision, recall, mAP @ 0. 5, mAP @ 0. 5: 0. 95 of the improved eye detection model were 95.8%, 92.7%, 94.8% and 73. 0%, respectively. The model size was 40.4 MB and operated at a detection speed of 37. 03 frames per second. Compared with similar object detection models of YOLO v4, YOLO v5, YOLO v6, YOLO v7 and YOLO v8n, the detection accuracy was 4.5, 3. 1, 5.2, 4.7 and 4. 1 percentage points higher, respectively, and the detection effect was better than other models. Moreover, it exhibited lower rates of false detections (3.7%) and missed detections (1.5%) compared with other models. These enhancements ensured the model’s capability to detect potato seed bud eyes effectively under diverse conditions, offering theoretical support for the development of intelligent seed cutting machines, which could revolotionize agricultural automation in the near future. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 19

Main heading: Damage detection

Controlled terms: Agricultural machinery? - ?Agriculture? - ?Data acquisition? - ?Eye protection? - ?Information fusion? - ?Object detection? - ?Object recognition

Uncontrolled terms: Bud eye detection? - ?Detection effect? - ?Detection models? - ?Efficient multi-scale attention? - ?Eye detection? - ?Loss functions? - ?Multi-scales? - ?Potato seed? - ?Targets detection? - ?YOLO v8

Classification code: 821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?903.1 Information Sources and Analysis? - ?913.3.1 Inspection? - ?914.1 Accidents and Accident Prevention? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 0.00E00%, Percentage 1.50E+00%, Percentage 3.70E+00%, Percentage 9.27E+01%, Percentage 9.48E+01%, Percentage 9.58E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.043

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

31. Fusion Model of Evapotranspiration of Summer Maize for Scale Conversion of Multi-source Remote Sensing Data

Accession number: 20253519071927

Title of translation: 多源遥感数据尺度转换的夏玉米蒸散发融合模型研究

Authors: Hu, Xiaotao (1, 2); Liu, Chang (1, 2); Wang, Yakun (1, 2); Li, Gaoliang (1, 2); Dai, Qin (1, 2); Chen, Hong (1, 2)

Author affiliation: (1) Key Laboratory of Agricultural Soil and Water Engineering in Arid Areas, Northwest A&F University, Ministry of Education, Shaanxi, Yangling; 712100, China; (2) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 21-31

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Evapotranspiration (ET) is a core element of crop water requirement and serves as a key basis for optimizing regional water resource allocation. Focusing on summer maize in the Baojixia Irrigation District located in the Guanzhong Plain of Shaanxi Province, four machine learning algorithms were employed, back propagation neural network (BPNN) , support vector machine (SVM) , extreme learning machine (ELM) , and eXtreme gradient boosting (XGBoost) , to develop a collaborative correction model utilizing multisource remote sensing data from unmanned aerial vehicles ( UAVs ) and satellites. Subsequently, the model constructed by the optimal algorithm was selected to correct satellite multispectral data, ultimately achieving scale conversion between U AV and satellite data. The calibrated high-precision satellite data were utilized to retrieve the leaf area index (LAI) and crop height ( hc ) of summer maize, providing essential data inputs for the evapotranspiration (ET) model. Evapotranspiration (ET) of summer maize was estimated using three distinct approaches; the dual crop coefficient method, the mapping evapotranspiration at high resolution with internalized calibration ( METRIC ) model, and the Penman - Monteith ( P - M ) canopy resistance model. Subsequently, the Bayesian model averaging (BMA) method was introduced to dynamically assign weights to each method/model across different growth stages. Ultimately, this process led to the development of a robust BMA-merged ET model for the maize growing period spanning from the jointing stage to physiological maturity. The results demonstrated that the XGBoost algorithm consistently achieved the highest modeling accuracy for 5/G/fí/NIR bands during the jointing to maturity stages of summer maize, with R2 values in the four-band modeling outperforming the suboptimal ELM algorithm by 8.43% , 8. 67% , 6. 79% , and 10.41% respectively. The retrieval of LAI and hc using the calibrated satellite multispectral data exhibited an average improvement in R2 of 97% and 67.5% , respectively, compared with retrievals based on the original satellite data. Compared with the single best-performing method/model (the METRIC model) , the BMA-merged model significantly reduced the root mean squared error ( RMSE ) by 39. 3% to 58. 5% during both the jointing-tasseling stage and the dough-physiological maturity stage of summer maize. The “collaborative calibration-dynamic fusion” framework proposed significantly improved the accuracy of remote sensing-based evapotranspiration (ET) monitoring, thereby providing theoretical support for precision water resource management. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Evapotranspiration

Controlled terms: Agricultural machinery? - ?Antennas? - ?Backpropagation? - ?Barium compounds? - ?Bayesian networks? - ?Calibration? - ?Crops? - ?Cultivation? - ?Grain (agricultural product)? - ?Irrigation ? - ?Knowledge acquisition? - ?Learning systems? - ?Mean square error? - ?Physiological models? - ?Remote sensing? - ?Resource allocation? - ?Satellites? - ?Statistical tests? - ?Support vector machines? - ?Unmanned aerial vehicles (UAV)

Uncontrolled terms: Aerial vehicle? - ?Bayesian model averaging? - ?Calibration model? - ?Collaborative calibration model? - ?Satellite data? - ?Satellite remote sensing? - ?Scale conversions? - ?Summer maize? - ?Unmanned aerial vehicle

Classification code: 101.1 Biomedical Engineering? - ?444 Water Resources? - ?652.1 Aircraft? - ?655.1 Satellites? - ?716.5.1 Antennas? - ?731.1 Control Systems? - ?804.2 Inorganic Compounds? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?912.2 Management? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1201.5 Computational Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1202.2 Mathematical Statistics? - ?1502.3 Hydrology

Numerical data indexing: Percentage 1.041E+01%, Percentage 3.00E+00% to 5.80E+01%, Percentage 5.00E+00%, Percentage 6.70E+01%, Percentage 6.75E+01%, Percentage 7.90E+01%, Percentage 8.43E+00%, Percentage 9.70E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.002

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

32. Effect of Alternate Irrigation with Reclaimed Water and Saline Water on Sunflower Growth and Grain Development

Accession number: 20253519059793

Title of translation: 再生水与微咸水轮灌对向日葵生长和籽粒发育的影响

Authors: He, Pingru (1, 2); Li, Jingang (2); Chen, Dan (2); Chen, Jing (1, 2); Jin, Qiu (3); Ding, Siyu (2)

Author affiliation: (1) College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing; 210098, China; (2) College of Agricultural Science and Engineering, Hohai University, Nanjing; 210098, China; (3) Nanjing Hydraulic Research Institute, Nanjing; 210029, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 555-566

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The fresh water resources for irrigation are severely scarce in arid and semi-arid agricultural areas, thus the intensive irrigation with reclaimed water resources and underground saline water resources have become a key issue for sustainable development of regional crop farming. According to the key growth stages of sunflower (seedling — squaring stage, squaring — flowering stage, flowering — maturity stage), three groups of continuous irrigation modes (FFF, SSS, RRR) and six groups of alternate irrigation modes (SSR, SRS, RSS, SRR, RSR, RRS) were set up, with fresh water (F), saline water (S) and reclaimed water (R). Two-year field experiment was carried out in Yinbei Irrigation District of Ningxia to explore the effects of different rotation irrigation modes with reclaimed water and saline water on the plant growth characteristics and grain development characteristics of sunflower. The results indicated that the degree of abiotic stress caused by reclaimed water irrigation at sunflower peak flowering stage was lower than that of irrigation with saline water. At the seedling — flowering stage of sunflower, the saline water irrigation promoted the plant dry matter distribution to roots, while the reclaimed water irrigation promoted the plant dry matter distribution to stems, leaves and faceplates. Reclaimed water irrigation at sunflower seeding — flowering stage can significantly improve the content of crude protein, unsaturated fatty acids and saturated fatty acids in grains. The SRR alternate irrigation mode significantly increased the sunflower grain yield by 2. 9% ~ 32. 7%, compared with other rotation irrigation modes. While, the water use efficiency for RRS alternate irrigation mode was significantly decreased by 19. 9% ~ 28. 1%, the nitrogen and phosphorus use efficiency was reduced by 22. 4% ~ 38. 0%, compared with other rotation irrigation modes. Reclaimed water irrigation from flowering to maturity stage was more conducive to improving sunflower seed yield and water use efficiency than brackish water irrigation. Moreover, in a certain number of years, alternate irrigation with treated reclaimed water and saline water would not lead to excessive accumulation of heavy metal elements in sunflower grains. The research result suggested that the mode of saline water irrigation at seeding — squaring stage and reclaimed water irrigation at squaring - maturity stage could effectively save fresh water, promote sunflower plant growth and improve the grain quality, which could provide reference for the development and utilization of reclaimed water and saline water in water-deficient agricultural areas of Northwest China. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Efficiency

Controlled terms: Crops? - ?Grain (agricultural product)? - ?Grain growth? - ?Nitrogen? - ?Phosphorus? - ?Plants (botany)? - ?Reclamation? - ?Saline water? - ?Seed? - ?Subirrigation ? - ?Sustainable development? - ?Water conservation? - ?Water treatment

Uncontrolled terms: Abiotic stress? - ?Agricultural areas? - ?Crude proteins? - ?Flowering stage? - ?Grain development? - ?Irrigation modes? - ?Maturity stages? - ?Mulched drip irrigations? - ?Re-claimed water? - ?Sunflower

Classification code: 103 Biology? - ?444 Water Resources? - ?445.1 Water Treatment Techniques? - ?804 Chemical Products? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?913.1 Production Engineering? - ?1301.4.1.2 Crystal Growth? - ?1501.1 Sustainable Development? - ?1501.2.1 Resource Conservation? - ?1501.3 Sustainable Waste Managment

Numerical data indexing: Percentage 0.00E00%, Percentage 1.00E00%, Percentage 4.00E+00%, Percentage 7.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.08.053

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

33. Phenotype Data Measurement and Quality Assessment of Largemouth Bass (Micropterus salmoides) Based on Improved Deeplabv3+

Accession number: 20253519059797

Title of translation: 基于改进 Deeplabv3+ 的大口黑鲈表型数据测量与品质预测方法

Authors: Feng, Guofu (1, 2); Zeng, Zhichao (1, 2); Wang, Wenjuan (1, 2); Wang, Yaohui (3); Wang, Hao (1, 2)

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) Zhongyang Seed(Jiangsu) Co., Ltd., Nantong; 226600, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 517-525

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Phenotype data, weight, and condition factor of largemouth bass served as crucial basic information, providing a direct insight into the fish’s growth and health status in fishery aquaculture. In response to the problems of cumbersome and inefficient manual measurement of the above data, and the lack of area elements in phenotype data measurement methods based on key point, a phenotype data measurement method was proposed based on Deeplabv3 + . And quality assessment was completed based on phenotype data measurement results. First of all, based on the analysis of the morphological characteristics of visible parts of the largemouth bass, the fish was divided into four parts; head, trunk, fins, and tail. Each of these parts was manually annotated with semantic labels by using Labelme software. Different batches images of largemouth bass were used as the dataset, and following the process of data enhancement, the dataset reached 1 095 pieces, and the ratio of training set to validation set was 9: 1. Secondly, using convolutional block attention module (CBAM) and squeeze-and-excitation network (SENet) to improve the Deeplabv3 +, the CBAM module adjusted feature map weights adaptively by utilizing channel attention and spatial attention, enabled the network to concentrate on the morphological features of largemouth bass, thereby enhanced segmentation accuracy. The SENet module mitigated channel redundancy in Deeplabv3 network feature maps, thereby enhanced both parameter and computational efficiency. With the help of the above modules, the overall model could achieve high-precision segmentation of the head, trunk, tail, and fins of largemouth bass. Subsequently, the segmentation results were used to measure the length, height of each part by using the minimum axis-aligned bounding box. And the area was estimated based on the ratio of pixels in each part to box pixels. The area was fitted by using actual measured phenotype data, and the results were used as standard values to evaluate the accuracy of area estimation. Three commonly used fitting models were used to fit the weight of largemouth bass based on measurement results, and the best fitting model could be found by comparing the fitting results. Finally, the condition factors were computed by using body length and weight, and compared with actual condition factors to further validate the accuracy of the measurement data. The experimental result showed that the overall mloU of the semantic segmentation model reached 90. 15%, and after ignoring the influence of fish fins, the mloU reached 94.02%. The mean relative errors of the measured total length (TL), body length (BL), body height (BH), head length (HL) and head height (HH) were less than 3. 5% . The mean relative error of area estimation was less than 4. 5% . The correlation coefficient between the predicted and actual weight values by using polynomial models was 0. 97, with the mean relative error less than 4% . The calculated results of the three condition factors based on the measured values were close to the actual values. This method could efficiently and accurately obtain phenotype data of largemouth bass and predict its growth status, providing a reference for the study of fish growth and health. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Computer vision

Controlled terms: Computational efficiency? - ?Convolution? - ?Data accuracy? - ?Fins (heat exchange)? - ?Fish? - ?Fisheries? - ?Image enhancement? - ?Large datasets? - ?Mathematical morphology? - ?Redundancy ? - ?Semantic Segmentation? - ?Semantics

Uncontrolled terms: Condition? - ?Data measurements? - ?Deeplabv3 +? - ?Feature map? - ?Largemouth bass? - ?Mean relative error? - ?Measurement methods? - ?Phenotype data? - ?Quality assessment? - ?Semantic segmentation

Classification code: 306.1 Heat Exchange Equipment and Components? - ?471.5 Sea as Source of Minerals and Food? - ?716.1 Information Theory and Signal Processing? - ?822 Food Technology? - ?822.3 Food Products? - ?903.2 Information Dissemination? - ?913.3 Quality Assurance and Control? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory

Numerical data indexing: Percentage 1.50E+01%, Percentage 4.00E+00%, Percentage 5.00E+00%, Percentage 9.402E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.049

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

34. Soil Moisture Estimation of Wheat Fields by Optimizing Dry and Wet Boundaries of Temperature Vegetation Drought Index in Huaihe Basin

Accession number: 20253519069898

Title of translation: 基于优化温度植被干旱指数干湿边界的淮河流域麦田墒情反演

Authors: Chen, Pengyu (1); Zhai, Yarning (1); Huang, Mingyi (1); Zhu, Chengli (1); Du, Wei (1); Tu, Xin (1)

Author affiliation: (1) College of Agricultural Science and Engineering, Hohai University, Nanjing; 210098, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 128-141

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The regional soil moisture estimation based on the temperature vegetation dryness index (TVDI) holds significant potential for drought monitoring and water resource planning in basins. However, the empirical nature and uncertainty in quantifying the dry and wet boundaries within the TVDI feature space can easily limit the accuracy of the estimation. A multi-objective optimization method was introduced to address the determination of TVDI’s dry-edge and wet-edge. By maximizing the correlation between TVDI and both surface albedo as well as soil red/near-infrared reflectance, a quantitative solution for the dry and wet edges in the feature space was achieved, enabling an analysis of soil moisture inversion in wheat fields within the Huai River Basin. Results indicated that during the optimization of the TVDI dry and wet edges, surface albedo significantly enhanced the accuracy of soil moisture inversion, accounting for a weight of 0. 5 ~ 0. 8. The contributions from soil red wave and near-infrared reflectivity were relatively smaller, at 0. 1 ~ 0. 2 and 0. 1 ~ 0. 3 , respectively. The optimized TVDI demonstrated improved responsiveness to changes in meteorological drought during the wheat growing period. The coverage range of the feature space was expanded by 24. 05% ~ 54. 02% , with the intercept of the dry edge increased by 1. 72% ~5. 69% and the slope decreased by 8. 04% ~ 66. 51% . After optimization, the coefficient of determination ( R ) between TVDI and measured soil moisture content was increased by 33. 12% ~ 82. 61% . Meanwhile, the average absolute error (MAE) , root mean square error (RMSE) , and normalized root mean square error ( NRMSE ) during soil moisture estimation was decreased by 5. 09% ~ 20. 52% , 7. 73% ~ 21. 16% , and 7. 69% ~ 21. 27% , respectively, ensuring high accuracy across different growth stages and soil layer depths. In 2023 , the average soil moisture content at 0 ~ 40 cm3 depth during the jointing, booting, flowering, and grain-filling stages of winter wheat in the Huai River Basin was 0. 242 cm3 /cm3 , 0. 255 cm3 /cm3 , 0. 259 cm3 /cm3 , and 0. 237 cm3 /cm3 , respectively. Wheat fields in Henan Province and Shandong Province exhibited relatively low soil moisture levels, making supplementary irrigation advisable during the jointing, flowering, and grain-filling stages. In conclusion, the multi-objective optimization method for determining the dry and wet edges improved the adaptability and precision of TVDI for regional-scale soil moisture inversion in wheat fields. The research result can provide a novel theoretical foundation and reliable tools for drought monitoring and prevention research. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 43

Main heading: Soil moisture

Controlled terms: Crops? - ?Cultivation? - ?Drought? - ?Errors? - ?Infrared devices? - ?Infrared radiation? - ?Mean square error? - ?Moisture determination? - ?Multiobjective optimization? - ?Reflection ? - ?Soil surveys? - ?Uncertainty analysis? - ?Water resources

Uncontrolled terms: Boundary determination? - ?Drought index? - ?Dry and wet? - ?Feature space? - ?Huaihe basin? - ?NSGA-II? - ?Soil moisture estimation? - ?Temperature-vegetation dryness indices? - ?Wheat fields? - ?Winter wheat

Classification code: 405.3 Surveying? - ?443.3 Precipitation? - ?444 Water Resources? - ?483.1 Soils and Soil Mechanics? - ?731.1.1 Error Handling? - ?741.1 Light/Optics? - ?741.3 Optical Devices and Systems? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?941.6 Moisture Measurements? - ?1201.7 Optimization Techniques? - ?1202.1 Probability Theory? - ?1202.2 Mathematical Statistics? - ?1301.3 Optics

Numerical data indexing: Percentage 1.20E+01%, Percentage 1.60E+01%, Percentage 2.00E+00%, Percentage 2.70E+01%, Percentage 4.00E+00%, Percentage 5.00E+00%, Percentage 5.10E+01%, Percentage 5.20E+01%, Percentage 6.10E+01%, Percentage 6.90E+01%, Percentage 7.20E+01%, Percentage 7.30E+01%, Percentage 9.00E+00%, Size 0.00E00m to 4.00E-01m, Size 2.37E+00m, Size 2.42E+00m, Size 2.55E+00m, Size 2.59E+00m

DOI: 10.6041/j.issn.1000-1298.2025.08.012

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

35. Design and Experiment of Sowing Depth Regulation System for High-speed Lightweight No-till Planter Based on Pneumatic Downforce Control

Accession number: 20253519069946

Title of translation: 基于气动下压力控制的高速轻型免耕播种机播深调控系统设计与试验

Authors: Chen, Haitao (1, 2); Yu, Chenpeng (1, 3); Wang, Yu (1, 3); Wang, Xing (1, 3); Liu, Liyi (1, 3); Shang, Jiajie (1, 3); Song, Xingtao (1, 3)

Author affiliation: (1) College of Engineering, Northeast Agricultural University, Harbin; 150030, China; (2) College of Mechanical and Electronic Engineering, East University of Heilongjiang, Harbin; 150066, China; (3) Heilongjiang Province Technology Innovation Center of Mechanization and Materialization of Major Crops Production, Harbin; 150030, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: 2025

Publication year: 2025

Pages: 229-238

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of poor stability of sowing depth in the high-speed operation of conventional planter and low sensitivity of the assembly of profiled wheel-based under-pressure sensing sowing depth regulation system, a high-speed lightweight no-till planter sowing depth regulation system was designed based on pneumatic under-pressure control to improve the quality of its high-speed sowing operation. The designed system mainly included monitoring device, decision control system, sowing depth control actuator and protection device. The monitoring device utilized the angle sensor of the parallel four-bar profiling mechanism, the film pressure sensor of the profiling pressure wheel and the attitude sensor of the planter to realize the real-time monitoring of the sowing depth; the decision-making control system processed the sensor data and controlled the internal air pressure of the air spring through the solenoid valve and the electrical proportional valve to ensure that the actuator’s output force was regulated in real time. In order to improve the accuracy of the output force of the actuator of the sowing depth control system, a model of the relationship between the output force of the air spring of the sowing depth control system and the lower pressure was established, which changed the output force of the air spring in real time through the change of the pressure under the imitation suppressor wheel monitored by the thin-film pressure sensor, and the model coefficient of determination R was 0. 995 9. A step response test was carried out for the step response of the sowing depth control system of the high-speed and light no-tillage planter. Tests were conducted, and the results showed that the maximum overshoot of the system was 6. 40% , the average response time was 0. 3 s, the average steady state error was 0. 004 7 MPa, the average standard deviation of the steady state value was 0.002 7 MPa, and the average coefficient of variation was 0. 93% for pressures ranging from 0. 1 MPa to 0. 6 MPa. A comparative field performance test was conducted with the parallel four-bar profiling spring-type depth control system as the control group, and the results showed that under high-speed operating conditions (14-16 km/h) , the average seeding depth qualification rate of the light-duty no-till planter retrofitted with this seeding depth control system was 81% , which was 44. 64% higher than that of the mechanical spring-type downward pressure control mode (56% ) ; the standard deviation of the sowing depth was an average of 4. 87 mm, and the coefficient of variation averaged 14. 08% , compared with the mechanical spring-loaded regulation method, which were reduced by 21. 52% and 29. 35% , respectively. The research results can provide theoretical reference for the improvement of sowing depth stability of high-speed lightweight no-till planter. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Solenoid valves

Controlled terms: Actuators? - ?Agricultural machinery? - ?Decision making? - ?Depth profiling? - ?Pneumatics? - ?Pressure sensors? - ?Proportional control systems? - ?Response time (computer systems)? - ?Seed? - ?Sensitivity analysis ? - ?Solenoids? - ?Springs (components)? - ?Step response? - ?Wheels

Uncontrolled terms: Airsprings? - ?Depth control system? - ?Down pressure? - ?High Speed? - ?Lightweight no-till planter? - ?No-till planters? - ?Output force? - ?Pneumatic down pressure? - ?Seeding depth? - ?Seeding depth stability regulation

Classification code: 601.2 Machine Components? - ?610 Pipes, Tanks and Accessories? - ?704.1 Electric Components? - ?731.1 Control Systems? - ?732.1 Control Equipment? - ?802 Chemical Apparatus and Plants; Unit Operations; Unit Processes? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?912.2 Management? - ?942.1.9 Pressure Measuring Instruments? - ?1106.3 Digital Signal Processing? - ?1201 Mathematics? - ?1401.3 Pneumatics, Equipment and Machinery

Numerical data indexing: Percentage 3.50E+01%, Percentage 4.00E+01%, Percentage 5.20E+01%, Percentage 5.60E+01%, Percentage 6.40E+01%, Percentage 8.00E+00%, Percentage 8.10E+01%, Percentage 9.30E+01%, Pressure 1.00E+06Pa to 0.00E00Pa, Pressure 6.00E+06Pa, Pressure 7.00E+06Pa, Size 1.40E+04m to 1.60E+04m, Size 8.70E-02m, Time 3.00E+00s

DOI: 10.6041/j.issn.1000-1298.2025.08.021

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

36. Detection of Tomato Leaf Mould Latency Based on Hyperspectral Imaging

Accession number: 20253519078001

Title of translation: 基于高光谱成像的番茄叶霉病潜伏期检测

Authors: Zhao, Dayong (1); Zhang, Yiwen (1, 2); Wang, Zhuo (1); Bai, Xiaoping (3); Wang, Xiaoxiong (1, 2)

Author affiliation: (1) Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang; 110016, China; (2) School of Computer Science and Technology, University of Chinese Academy of Science, Beijing; 100049, China; (3) School of Physics, Liaoning 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: 8

Issue date: August 2025

Publication year: 2025

Pages: 390-397

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The rapid spread of tomato leaf mould can lead to significant losses, and timely detection and identification of the disease is of great importance. Totally 170 tomato leaf mould latent stage day 5 sample curves and 170 tomato healthy sample curves were labeled and extracted based on hyperspectral imaging. Spectral pre-processing of the data was done by four methods; min-max normalization (MMN), standard normal variate (SNV), wavelet transform (WT) and baseline correction (BC), and abnormal samples were rejected by using a clustering algorithm (K-means). The competitive adaptive reweighted sampling (CARS) algorithm was used for feature band selection, and the selected single feature bands were analyzed qualitatively and quantitatively. Finally, two classification methods, namely support vector machine (SVM) and linear discriminant analysis (LDA), were employed to identify leaf mould latent stage samples and healthy samples, and a total of eight machine learning-based identification models for tomato leaf mould latent stage detection were constructed and compared to find the optimal model. The results showed that the feature bands selected by the CARS algorithm had a positive effect on the overall recognition, and the WT - CARS - LDA model performed the best, with an accuracy of 97. 62%. The hyperspectral imaging technology was combined with the machine learning method, and a highly efficient and accurate identification model was successfully constructed for tomato leaf mould potential stage detection. The research result can provide a feasible technical solution for early detection and control of tomato leaf mould. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Hyperspectral imaging

Controlled terms: Chemical detection? - ?Cluster analysis? - ?Clustering algorithms? - ?Data mining? - ?Discriminant analysis? - ?Fruits? - ?Learning systems? - ?Molds? - ?Plants (botany)? - ?Support vector machines ? - ?Wavelet transforms

Uncontrolled terms: Disease latency identification? - ?Feature bands? - ?HyperSpectral? - ?Identification modeling? - ?Linear discriminant analyze? - ?Machine-learning? - ?Sampling algorithm? - ?Tomato leaf? - ?Tomato leaf mold? - ?Wavelets transform

Classification code: 103 Biology? - ?746 Imaging Techniques? - ?802 Chemical Apparatus and Plants; Unit Operations; Unit Processes? - ?821.5 Agricultural Products? - ?903.1 Information Sources and Analysis? - ?913.4 Manufacturing? - ?1101.2 Machine Learning? - ?1106.2.1 Data Mining? - ?1201.3 Mathematical Transformations? - ?1202 Statistical Methods

Numerical data indexing: Percentage 6.20E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.036

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

37. UAV Multispectral Remote Sensing and Machine Learning-based Growth Inversion for Cinnamomum camphora

Accession number: 20253519072542

Title of translation: 基于无人机多光谱遥感和机器学习的香樟生长反演

Authors: Zhang, Yue (1, 2); Zhang, Haina (1, 2); Lu, Xianghui (1, 2); Zhang, Jie (1, 2); Wan, Haolong (1, 2); Luo, Xin (1, 2)

Author affiliation: (1) School of Water and Soil Conservation, Jiangxi University of Water Resources and Electric Power, Nanchang; 330099, China; (2) Jiangxi Provincial Engineering Research Center for Camphor Tree Breeding and Development, Jiangxi University of Water Resources and Elecric Power, Nanchang; 330099, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 380-389

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: UAV multispectral technology was demonstrated to facilitate rapid and accurate acquisition of growth parameters for Cinnamomum camphora (L.) presl var. linaloolifera Fujita, providing technical support for precision management of its dwarf forests. Cinnamomum camphora dwarf forests in the southern red soil region were investigated. Multispectral canopy remote sensing images were acquired by using a multispectral camera, while field measurements were conducted to obtain leaf chlorophyll content (SPAD), leaf area index (LAI), and above-ground biomass (AGB) data. A comprehensive growth monitoring index (CGMI) was then constructed through the entropy weight method. Six machine learning algorithms, support vector machine (SVM), back propagation neural network (BPNN), radial basis function neural network (RBFNN), decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost), were employed to invert SPAD, LAI, AGB, and CGMI values of Cinnamomum camphora. The inversion accuracy of single indices and the CGMI model was compared, and the optimal model was selected. The results demonstrated that all CGMI-based inversion models outperformed single-index models. The test set coefficient of determination (R2 ) ranged from 0. 614 to 0.862, while the root mean square error (RMSE) ranged from 0.074 to 0.953. Among CGMI inversions, the XGBoost model achieved the highest accuracy (R2 was 0. 862, RMSE was 0. 092). In conclusion, CGMI inversion accurately assessed the growth status of Cinnamomum eamphora dwarf forests, with XGBoost being the optimal model. The research result can provide a reference for UAV multispectral-based growth monitoring of such forests. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Radial basis function networks

Controlled terms: Backpropagation? - ?Decision trees? - ?Forestry? - ?Learning systems? - ?Multilayer neural networks? - ?Support vector machines? - ?Value engineering

Uncontrolled terms: Cinnamomum camphora? - ?Cinnamomum eamphora? - ?Growth indices? - ?Growth monitoring? - ?Machine learning algorithms? - ?Monitoring index? - ?Multi-spectral? - ?Multispectral remote sensing? - ?UAV multispectral remote sensing? - ?Vegetation index

Classification code: 821.1 Woodlands and Forestry? - ?911.5 Value Engineering? - ?961 Systems Science? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1201.5 Computational Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory

DOI: 10.6041/j.issn.1000-1298.2025.08.035

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

38. Mechanism of Long-term Aerated Infiltration Irrigation to Enhance Soil Environment and Increase Corn Yield

Accession number: 20253519059795

Title of translation: 长期加气渗灌提升土壤环境与玉米增产机理研究

Authors: Yu, Zhenzhen (1); Yin, Ningxia (1); Wang, Hongxuan (2); Li, Chenggang (1); Kang, Zhiwei (1); Yu, Deshui (3)

Author affiliation: (1) School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang; 524088, China; (2) South Subtropical Crops Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang; 524088, China; (3) School of Management, Huazhong University of Science and Technology, Wuhan; 430074, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 589-601

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to study the effects of long-term oxygenation and irrigation technology on soil fertility changes and maize yield under maize continuous cropping system in red loam soil, and explore the mechanism of maize yield increasement under this technology, which can provide theoretical basis for crop quality and yield increasement under aerated infiltration technology, relied on the 7 a (2017—2023) National Soil Quality Zhanjiang Observatory Experimental Station aerated infiltration experimental base, two treatments of traditional subsurface infiltration irrigation (CK) and aerated infiltration irrigation (01) were set up, and soil aeration, soil fertility, root growth, physiological characteristics and yield indexes under different treatments were determined. The results showed that the 01 treatment, which was implemented continuously for 7 a, improved soil aeration and soil fertility indexes compared with CK, and could significantly and very significantly increase soil oxygen content, soil respiration rate, soil bacterial biomass and soil urease activity, with increases of 2. 13% -22.89%, 1.86% -26.67%, 28.71% -49.51% and 13.59% -103.93%, respectively. On the basis of improving soil aeration and fertility, root length, root surface area and root dry weight were increased by 28. 08% -55. 69%, 24. 92% -73. 00% and 17. 16% -44. 86% (P ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 55

Main heading: Infiltration

Controlled terms: Agronomy? - ?Crops? - ?Grain (agricultural product)? - ?Oxygen? - ?Physiology? - ?Plants (botany)? - ?Soil conditioners? - ?Soil quality? - ?Subirrigation

Uncontrolled terms: Aerated infiltration irrigation? - ?Corn? - ?Maize yield? - ?Root growth? - ?Soil aeration? - ?Soil fertility? - ?Soil respiration rates? - ?Structural equation models? - ?Yield increase? - ?Yield increase mechanism

Classification code: 103 Biology? - ?483.1 Soils and Soil Mechanics? - ?804 Chemical Products? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products

Numerical data indexing: Percentage 0.00E00%, Percentage 1.30E+01% to 2.289E+01%, Percentage 1.359E+01% to 1.0393E+02%, Percentage 1.60E+01% to 1.442E+01%, Percentage 1.60E+01%, Percentage 1.86E+00% to 2.667E+01%, Percentage 2.871E+01% to 4.951E+01%, Percentage 6.90E+01%, Percentage 8.00E+00%, Percentage 8.60E+01%, Percentage 9.20E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.056

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

39. Wheel Coupling Testing Method for Field Operating Conditions of Large Combine Harvester

Accession number: 20253519069955

Title of translation: 大型联合收获机田间作业工况轮耦合试验方法研究

Authors: Yang, Zihan (1, 2); Gu, Yuanyang (1); Guo, Zhiqiang (1, 2); Wang, Bin (1); Lin, Hengchu (2); Zhu, Jiangpeng (2); Yu, Zhiwei (2); Song, Zhenghe (1)

Author affiliation: (1) Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, China Agricultural University, Beijing; 100083, China; (2) National Innovation Agricultural Equipment Quality Test Technology (Luoyang) Co., Ltd., Luoyang; 471000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 303-313

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The field working conditions of combine harvester are complex, and the interactions between soil, machine, and material create complex excitations, which pose a significant challenge to the durability and reliability of the whole machine. Therefore, effectively simulating the operating conditions of the combine harvester under laboratory conditions is crucial for enhancing the standardization of durability verification. The durability testing method for the entire combine harvester was explored based on the wheel coupling test bench. A load data acquisition system was developed to assess the field operation surface excitation. Vibration characteristic analysis of the whole machine under different operating conditions was carried out based on the measured data. A test procedure for the combine harvester wheel coupling test rig was established, and bench test simulations were conducted for the typical operating condition. The results showed that the simulation accuracy of the load data for each target iterative channel under the harvesting operation conditions of the combine harvester was above 91%. The simulated load signal and the measured load signal had consistent power spectral density distribution characteristics. Furthermore, the pseudo-damage reproduction ratio of each chassis strain channel reflecting the vertical road excitation met the experimental requirements of 50% ~ 200% . In conclusion , the wheel coupling test bench was capable of simulating and applying vertical excitation loads for the combine harvester, offering more convenient technical support for structural durability testing in the agricultural machinery field. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Durability

Controlled terms: Agriculture? - ?Combines? - ?Couplings? - ?Data acquisition? - ?Harvesters? - ?Iterative methods? - ?Soil testing? - ?Vibration analysis? - ?Wheels

Uncontrolled terms: Combine harvesters? - ?Durability test? - ?Durability testing? - ?Load spectrum? - ?Operating condition? - ?Test-bench? - ?Testing method? - ?Wheel coupling test bench? - ?Wheel couplings? - ?Whole machine

Classification code: 214 Materials Science? - ?214.1 Mechanical Properties of Materials? - ?483.1 Soils and Soil Mechanics? - ?601.2 Machine Components? - ?602.1 Mechanical Drives? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?941.5 Mechanical Variables Measurements? - ?1106.2 Data Handling and Data Processing? - ?1201.9 Numerical Methods? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Percentage 2.00E+02%, Percentage 5.00E+01%, Percentage 9.10E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.028

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

40. Development of Pork Color Scoring Models Based on Different Standards and Comparative Analysis of Their Measurement Accuracy

Accession number: 20253519059792

Title of translation: 不同标准板下猪肉肉色评分模型构建及其测定精度比较

Authors: Xiao, Dongsheng (1); Zhao, Sanqin (1); Wu, Wangjun (2); Li, Yongkang (1); Gu, Jiabing (1); Liu, Yutao (1); Huang, Ruihua (2); Li, Pinghua (2)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 211800, China; (2) College of Animal Science and Technology, Nanjing Agricultural University, Nanjing; 210095, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 684-691 and 725

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, high subjectivity, and fatigue associated with artificial scoring of pork color, as well as the poor consistency and comparability of objective scoring based on different pork color standard boards, a rapid construction method for pork color scoring models was proposed based on images of pork color standard boards. A systematic comparison of the scoring accuracy of pork color models based on different standard boards was conducted. Firstly, images of two pork color standard boards and 540 pork samples were collected by using a scanner, followed by a comparative analysis of subjective artificial scoring and objective scoring based on Euclidean distance for pork color. The L,a,b values of the pork color standard board images were extracted by using Matlab. Subsequently, ridge regression was applied to establish the ridge regression equations between the L,a,b values of the standard board images and their respective color grades. The intercepts of the equations were calibrated by using the pork sample images to obtain the pork color scoring models. The consistency and accuracy of the pork color scoring models based on different standard board images were evaluated by using the 540 pork samples. The results demonstrated that the scoring models based on the L parameter of the two standard panels achieved optimal consistency, with a consistency rate exceeding 92% and an accuracy rate exceeding 91% . In contrast, the scoring models based on the L,a parameters exhibited the highest scoring accuracy, achieving an accuracy rate exceeding 95%, but with a consistency rate only around 65%. The proposed pork color scoring models demonstrated high measurement precision, rapid parameter calibration, and strong consistency in measurement accuracy across different standard boards. The research result can provide theoretical and technical support for the development of precise, rapid, and automated pork color scoring systems. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Color

Controlled terms: Color codes? - ?Color image processing? - ?Colorimetry? - ?MATLAB? - ?Meats? - ?Parameter estimation

Uncontrolled terms: B value? - ?Color standards? - ?Color trait? - ?Comparative analyzes? - ?Measurement accuracy? - ?Model-based OPC? - ?Pork color? - ?Ridge regression? - ?Scoring models? - ?Standard scoring board

Classification code: 741.1 Light/Optics? - ?822.3 Food Products? - ?941.2 Optical Variables Measurements? - ?1106.3.1 Image Processing? - ?1106.5 Computer Applications? - ?1201 Mathematics? - ?1201.5 Computational Mathematics? - ?1202 Statistical Methods

Numerical data indexing: Percentage 6.50E+01%, Percentage 9.10E+01%, Percentage 9.20E+01%, Percentage 9.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.064

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

41. Dynamic Expansion Neighborhood Path Planning Method for Satellite Navigation Grader

Accession number: 20253519059696

Title of translation: 卫星导航平地机动态扩展邻域路径规划方法研究

Authors: Xi, Xiaobo (1); Jin, Jiajun (1); Wang, Yu (1); Han, Lianjie (1); Zou, Yunhan (1); Zhang, Ruihong (1)

Author affiliation: (1) School of Mechanical Engineering, Yangzhou University, Yangzhou; 225127, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 419-426 and 478

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the inefficiency caused by lengthy paths in current satellite navigation grader path planning methods, a path planning method of dynamic expansion neighborhood was proposed. Initially, based on a three-dimensional farmland model, grid sizes matching the leveling blade width were defined. The benchmark elevation was calculated, and the earthwork volume of the target farmland was determined by using the square grid weighted averaging method, and an earthwork grid map was generated. Subsequently, with the objective of improving leveling efficiency, path planning principles were established to prevent blade overload and no-load, reduce turning frequency, and minimize turning angles. A neighborhood matrix LnR was introduced, where LnR was dynamically expanded to search for all grids satisfying non-overload and non-no-load conditions. The next working grid with the minimal turning angle was selected. Finally, the processed grid data were updated, and the leveling status of corresponding grids was evaluated to achieve full-area coverage through single traversal while generating the optimal working path. Simulation results demonstrated that compared with S-shaped and outward spiral paths, the proposed method reduced path length by 66.4% and 75.6%, decreased turning frequency by 16. 9% and 39. 4%, and reduced turning angles by 14. 4% and 37. 6%, respectively. The working traversal count was only once, indicating high efficiency across different flatness requirements, particularly achieving rapid complete leveling under fine-flatness specifications. The research result can provide a reference for optimizing path planning methods in satellite navigation graders. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Motion planning

Controlled terms: Efficiency? - ?Expansion? - ?Farms? - ?Leveling (machinery)? - ?Navigation? - ?Rock mechanics? - ?Satellite navigation aids? - ?Satellites

Uncontrolled terms: Dynamic expansion? - ?Dynamic expansion neighborhood? - ?Land levelness? - ?Levelings? - ?Neighbourhood? - ?Path planning method? - ?Satellite navigation? - ?Satellite navigation grader? - ?Turning angles? - ?Turning frequencies

Classification code: 214 Materials Science? - ?435.1 Navigation? - ?481 Geology and Geophysics? - ?655.1 Satellites? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?913.1 Production Engineering? - ?1101 Artificial Intelligence

Numerical data indexing: Percentage 4.00E+00%, Percentage 6.00E+00%, Percentage 6.64E+01%, Percentage 7.56E+01%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.08.039

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

42. Analysis of Spatial and Temporal Evolution of Soil Salinity in Yellow River South Bank Irrigation Area of Dalate Banner, Inner Mongolia Based on Satellite Remote Sensing

Accession number: 20253519069951

Title of translation: 基于卫星遥感的内蒙古达拉特旗黄河南岸灌区土壤盐分时空演变分析

Authors: Wu, Yuxiao (1); Chen, Haorui (2, 3); Zhang, Baozhong (2, 3); Tao, Yuan (2, 3); Chen, Junying (1); Xu, Rigan (4); Li, Yun (4); Miao, Ping (5); Ma, Hongli (5); Xie, Mei (2, 3); Jing, Sisi (1)

Author affiliation: (1) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) China Institute of Water Resources and Hydropower Research, State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing; 100038, China; (3) National Center for Efficient Irrigation Engineering and Technology Research-Beijing, Beijing; 100048, China; (4) Ordos Water Conservancy Development Center, Ordos; 017200, China; (5) Ordos River and Lake Protection Center, Ordos; 017200, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 42-51 and 85

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Soil salinization is one of the major challenges to agricultural production, ecological environment and sustainable development of land resources in global arid and semi-arid regions. The irrigation area on the south bank of the Yellow River in Dalate Banner, Inner Mongolia has long been threatened by soil salinization. Studying the dynamic changes of soil salinity and its driving factors is of great guiding significance for agricultural production, ecological protection and water resource management in this area. Based on Sentinel - 2 satellite data and field soil salinity data, the soil salinity content ( SSC ) during the bare soil period and the vegetation coverage period was estimated through Pearson correlation coefficient analysis (PCC) , variable importance in projection analysis (VIP) , grey relational analysis ( GRA ) , and combined with three machine learning algorithms: backpropagation (BP) , random forest ( RF) , and support vector machine ( SVM ) . At the same time, based on the optimal inversion model and Landsat - 5 and Sentinel - 2 data, the spatio-temporal variation characteristics of soil salinity in the irrigation area from 2000 to 2024 were inverted and statistically analyzed. Finally, the driving effects of meteorological factors such as evaporation, precipitation, evapotranspiration ratio and temperature on soil salinity changes were analyzed. The results showed that the PCC - RF model performed best during the bare soil period (R2 of the modeling set was 0. 849, RMSE was 0. 118% , MAE was 0. 079% ; R2 of the validation set was 0. 753 , RMSE was 0. 158% , MAE was 0. 116% ). The spatio-temporal variation characteristics indicated that the area of saline soil continued to increase from 2000 to 2008 , while the water rights reform policy implemented from 2009 to 2016 effectively curbed the expansion of saline soil and converted a large area of saline soil into non-saline soil. Evaporation, precipitation, evapotranspiration ratio and temperature were the main meteorological influencing factors of soil salinity changes in the study area. The research result can provide a scientific basis for the monitoring and control of soil salinization in the irrigation area. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Main heading: Remote sensing

Controlled terms: Agricultural machinery? - ?Arid regions? - ?Correlation methods? - ?Environmental regulations? - ?Evapotranspiration? - ?Irrigation? - ?Learning systems? - ?Soil surveys? - ?Soil temperature? - ?Soils ? - ?Support vector machines? - ?Vegetation

Uncontrolled terms: Inner Mongolia? - ?Irrigation area? - ?Meteorological influencing factor? - ?Remote-sensing? - ?Saline soil? - ?Sentinel - 2? - ?Soil salinity? - ?Soil salinization? - ?Temporal and spatial variation? - ?Yellow river

Classification code: 103 Biology? - ?405.3 Surveying? - ?443 Meteorology? - ?444 Water Resources? - ?483.1 Soils and Soil Mechanics? - ?731.1 Control Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?1101.2 Machine Learning? - ?1202.2 Mathematical Statistics? - ?1502.1 Environmental Impact and Protection? - ?1502.3 Hydrology

Numerical data indexing: Percentage 1.16E+02%, Percentage 1.18E+02%, Percentage 1.58E+02%, Percentage 7.90E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.004

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

43. Spatiotemporal Evolution and Regulation Potential of Water-Crop-Ecosystem Coupling Coordination Degree in Shiyang River Basin

Accession number: 20253519069877

Title of translation: 石羊河流域水-作物-生态系统耦合协调度时空演变与调控潜力研究

Authors: Wei, Zheng (1, 2); Li, Daoxi (3); Liu, Jiaqi (1, 3); Zhang, Baozhong (1, 2); Pan, Yan (1, 2); Ju, Lemaomao (1, 2); Ren, Hao (1, 2)

Author affiliation: (1) China Institute of Water Resources and Hydropower Research, State Key Laboratory of River Basin Water Cycle Simulation and Control, Beijing; 100038, China; (2) Key Laboratory of Digital Twin River Basin, Ministry of Water Resources, Beijing; 100038, China; (3) School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou; 450045, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 142-151

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the contradiction between agricultural water scarcity and ecological conservation in the arid regions of Northwest China, the Shiyang River Basin was taken as its research area. Based on the pressure - state - response ‘ framework , a water resources - crop growth - ecological environment system evaluation framework comprising 12 indicators was established. The entropy weight method was employed to determine weights and conduct multicollinearity tests. Bayesian networks were introduced into the coupling coordination degree evaluation, achieving a methodological breakthrough from state description to causal diagnosis. Using multi-source remote sensing data to derive vegetation physiological parameters and environmental indicators, a monthly-scale analysis was conducted to characterize the system evolution patterns during the crop growth cycle from April to October 2023. The results indicated that the development indices of both the water resources and crop growth subsystems exhibited a single-peak curve trend with an initial rise followed by a decline, reaching peaks of 0. 64 and 0. 89 in June and July, respectively; the ecological environment subsystem development index continued to rise slowly ( from 0. 43 to 0. 62 ) ; the coupling coordination degree exhibited significant monthly variations, rising from 0.46 in April (facing imbalanced ) to a peak of 0.80 in July (moderately coordinated) , and then decreased to 0.57 in October (critically coordinated) , which was highly consistent with the crop growth cycle; the Bayesian network identified dynamic shifted in the dominant factors across different growth stages; irrigation-dominated in April, fertility-limited from May to July, and requiring coordinated regulation of water, and fertility from August to October; spatially, the central region maintained consistently high coordination levels, while the downstream Minqin area remained in a low-value zone. The research results can provide a comprehensive technical framework for precise management of agricultural water resources in arid regions, encompassing evaluation - diagnosis - regulation’. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Bayesian networks

Controlled terms: Abiotic? - ?Arid regions? - ?Barium compounds? - ?Biotic? - ?Crops? - ?Ecosystems? - ?Physiological models? - ?Remote sensing? - ?Research and development management? - ?Rivers ? - ?Water conservation? - ?Water management? - ?Watersheds

Uncontrolled terms: Bayesia n networks? - ?Coordination degree? - ?Coupling coordination? - ?Crop growth? - ?Remote sensing inversion? - ?Remote-sensing? - ?Shiyang river basins? - ?Spatiotemporal evolution? - ?Water - crop - ecosystem? - ?Waters resources

Classification code: 101.1 Biomedical Engineering? - ?407 Maritime and Port Structures; Rivers and Other Waterways? - ?443 Meteorology? - ?444 Water Resources? - ?731.1 Control Systems? - ?804.2 Inorganic Compounds? - ?821.5 Agricultural Products? - ?901.3 Engineering Research? - ?912.2 Management? - ?1201.5 Computational Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1501.2.1 Resource Conservation? - ?1502.2 Ecology and Ecosystems? - ?1502.3 Hydrology

Numerical data indexing: Size 1.1684E-02m, Size 1.4478E-02m, Size 2.032E-02m, Size 2.2606E+00m

DOI: 10.6041/j.issn.1000-1298.2025.08.013

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

44. Estimation Model of Comprehensive Moisture Index for Summer Maize Based on UAV Multispectral Data

Accession number: 20253519069878

Title of translation: 基于无人机多光谱数据的夏玉米综合水分指标估算模型

Authors: Wang, Yakun (1, 2); Ma, Yuxin (1, 2); Fan, Xiaodong (1, 2); Chen, Hong (1, 2); Hu, Xiaotao (1, 2)

Author affiliation: (1) Key Laboratory of Agricultural Soil and Water Engineering in Arid Areas, Northwest A&F University, Ministry of Education, Shaanxi, Yangling; 712100, China; (2) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 74-85

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate farmland moisture monitoring is vital for agricultural water conservation and yield protection. However, existing technologies mainly focus on single indicators like soil or leaf/plant water content, lacking a systematic characterization of soil - plant water collaborative mechanisms. Taking summer maize in the Guanzhong Plain as the research object, seven indicators were integrated, including multi-depth soil water content, leaf water content, and plant water content through ground sampling. Two comprehensive moisture indices, CMI1 (using the entropy weight method) and CMI2 (using principal component analysis) , were constructed to reflect the overall soil - plant moisture status. Sensitive vegetation indices were calculated and screened based on UAV multispectral data, and machine learning algorithms such as random forest (RF) and support vector machine (SVM) were applied to develop data-driven models for moisture estimation. The results showed that both CMI1 and CMI2 effectively reflected the comprehensive moisture status of summer maize farmland soil - plant systems, while CMI2 showed better characterization accuracy of soil - plant water coupling features than CMI1 in most growth stages (e.g. , jointing and silking stages). The response relationships between vegetation indices and comprehensive moisture indices varied dynamically with growth stages, and the highest correlation coefficients between optimal vegetation indices and CMI reached 0. 761 , 0. 795, 0. 769, and 0. 771 in the jointing, silking, grain-filling, and milky stages, respectively. The RF model exhibited more stable performance in both modeling and validation sets, with estimation accuracy superior to other models, enabling robust estimation of comprehensive moisture indices for summer maize. The research result presented a “ multi-index integration - UAV remote sensing - dynamic modeling” framework through dual performance comparisons of moisture indices and machine learning models, offering precise field-scale monitoring solutions for smart irrigation decisions. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Principal component analysis

Controlled terms: Agribusiness? - ?Agricultural machinery? - ?Couplings? - ?Farms? - ?Grain (agricultural product)? - ?Irrigation? - ?Learning algorithms? - ?Learning systems? - ?Moisture determination? - ?Remote sensing ? - ?Support vector machines? - ?Unmanned aerial vehicles (UAV)? - ?Vegetation? - ?Water content

Uncontrolled terms: %moisture? - ?Comprehensive moisture index? - ?Crop moisture status? - ?Machine-learning? - ?Moisture index? - ?Multi-spectrum? - ?Soil water content? - ?Summer maize? - ?UAV multispectrum? - ?Vegetation index

Classification code: 103 Biology? - ?601.2 Machine Components? - ?602.1 Mechanical Drives? - ?652.1 Aircraft? - ?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? - ?941.6 Moisture Measurements? - ?1101.2 Machine Learning

Numerical data indexing: Size 1.95834E+01m

DOI: 10.6041/j.issn.1000-1298.2025.08.007

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

45. Cotton Yield Estimation Based on UAV RGB Image-based Vegetation Indexes

Accession number: 20253519069893

Title of translation: 基于无人机RGB图像植被指数的棉花产量估算研究

Authors: Bai, Zhentao (1, 2); Dong, Bin Gxue (1); Fan, Junliang (1, 3); Shawn, Carlisle Kefauver (2); Jose, Luis Araus (2); Zhang, Fucang (1, 3); Yin, Feihu (3, 4)

Author affiliation: (1) Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest a and F University, Ministry of Education, Shaanxi, Yangling; 712100, China; (2) Faculty of Biology, University of Barcelona, Barcelona; 08028, Spain; (3) Institute of Farmland Water Conservancy and Soil Fertilizer, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi; 832000, China; (4) Key Laboratory of Northwest Oasis Water-saving Agriculture, Ministry of Agriculture and Rural Affairs, 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: 8

Issue date: August 2025

Publication year: 2025

Pages: 182-192

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to overcome the limitations of conventional remote sensing technologies that depend on multispectral or hyperspectral imaging, the potential of integrating RGB images with a color space conversion algorithm was explored for the purpose of crop yield estimation and monitoring. The canopy RGB images of drip-irrigated cotton acquired via UAV at six growth stages under 16 distinct water and nitrogen treatments were utilized. These images were converted into HIS, CIELab, and CIELuv color parameters through the implementation of a color space conversion algorithm. The most suitable yield estimation window was identified through a systematic selection process. Based on the derived RGB vegetation index, three machine learning algorithms, including ridge regression, support vector machine, and random forest were used to construct the drip-irrigated cotton yield in different growth stages under three different variable combinations. The findings demonstrated a robust correlation between the RGB vegetation index and cotton yield during various growth periods. The correlation was particularly pronounced in the flowering stage, flowering and boll stage I , flowering and boll stage E , boll-setting stag, and boll opening stage. The correlation between vegetation index and yield in the boll opening stage exhibited the strongest correlation, and the yield estimation accuracy in the boll opening stage was the highest (coefficient of determination was greater and equal to 0. 87 , deviation was less than 10% ) . The boll opening stage window demonstrated the optimal yield estimation. The inversion accuracy of the random forest machine model exhibited the most optimal comprehensive performance. The inversion result of the random forest model constructed by variable combination 3 ( GA, GGA, CSI, NGRDI, NGRDIveg, TGI, TGIveg, NDLab, NDLuv ) was the most optimal, with a test set determination coefficient of 0. 76 ~ 0. 88 , a root mean square error of 0. 69 ~ 0. 99 t/hm2 , a mean absolute error of 0. 53 ~ 0. 80 t/hm2 , and a deviation of 6. 11% ~ 30. 65% , which was the optimal inversion model for cotton yield under drip irrigation. The findings can serve as a theoretical foundation for the utilization of drone RGB images in the estimation of cotton yield from drip irrigation and the monitoring and analysis of phenotype. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 42

Main heading: Cotton

Controlled terms: Decision trees? - ?Errors? - ?Hyperspectral imaging? - ?Learning systems? - ?Mean square error? - ?Random forests? - ?Remote sensing? - ?RGB color model? - ?Space optics? - ?Subirrigation ? - ?Support vector regression? - ?Unmanned aerial vehicles (UAV)? - ?Vegetation

Uncontrolled terms: Color space conversion? - ?Conversion algorithm? - ?Cotton yield? - ?Irrigated cotton? - ?Machine-learning? - ?Random forests? - ?RGB images? - ?UAV remote sensing? - ?Vegetation index? - ?Yield estimation

Classification code: 103 Biology? - ?652.1 Aircraft? - ?655.2 Spacecraft Subsystems? - ?731.1 Control Systems? - ?731.1.1 Error Handling? - ?741.1 Light/Optics? - ?746 Imaging Techniques? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?961 Systems Science? - ?1101.2 Machine Learning? - ?1201.5 Computational Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 1.00E+01%, Percentage 1.10E+01%, Percentage 6.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.017

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

46. Estimation of Forest Canopy Height Overestimated by GEDI Weak Beam Received Waveforms in Different Terrain Slopes

Accession number: 20253519072442

Title of translation: 不同地形坡度下GEDI弱波接收波形对森林冠层高度估计的偏高

Authors: Cai, Longtao (1); He, Jiasheng (1); Wu, Jun (1); Xing, Zekun (1); Tian, Chengshuo (2)

Author affiliation: (1) School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin; 541004, China; (2) Remote Sensing Engineering and Data Application Center, Wuhan Academy of Surveying and Mapping, Wuhan; 430022, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 360-369 and 410

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The problem of low signal-to-noise ratio caused by low-energy emission laser pulses under weak beam conditions of spaceborne LiDAR (Light detection and ranging) GEDI (Global ecosystem dynamics investigation), as well as the overlap of forest canopy echoes caused by high-slope terrain with understorey terrain echoes, leading to underestimated accuracy of forest canopy height estimation. This study attempts to use quality screening fields in GEDI L2A product files to select data with high signal-to-noise ratio under weak beams, non-delayed echoes, non-descending echoes, and forest echoes, and then compare and analyze the overestimated forest canopy height under different percentage waveform length parameters Th_aN conditions to determine the applicable factors of the estimation model. Furthermore, terrain slope parameters DTM (Digital terrain model) are introduced to correct forest canopy height estimation models under terrain slope conditions of 0° -5°,0° ~ 10°,0° ~ 15°,0° ~ 20°,0° ~ 25°,0° ~ 30°,0° ~ >30°, and 0° -5°,5° ~ 10°,10° ~ 15M50 ~ 20°,20° ~25° -25° -30°, >30°, aiming to solve the problem of overlap between forest canopy echoes caused by high-slope terrain and understorey terrain echoes. The research results show that the single field quality_flag parameter is the same as the combination fields stale_return_flag, degrade_flag, quality_flag, and sensitivity parameters filtering results, and the retention rate of footprints after screening is 66. 60%. Selecting the waveform length field parameter rh_64 can achieve the highest overall accuracy of forest canopy height estimation in the study area, with R- and RMSE of 0. 556 2 and 4. 196 m respectively. After slope correction, the overall accuracy of forest canopy height estimation R’ and RMSE are 0. 566 5 and 4. 150 m respectively. The research results indicate that the overall accuracy of forest canopy height estimation decreases with the increase of terrain slope, while introducing terrain slope factors can improve the accuracy of forest canopy height estimation to a certain extent. Moreover, GEDI weak beam echoes can effectively estimate forest canopy height when the terrain slope is within 0° -20°. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Signal to noise ratio

Controlled terms: Ecosystems? - ?Forestry? - ?Landforms? - ?Optical radar? - ?Parameter estimation

Uncontrolled terms: Canopy heights? - ?Ecosystem dynamics? - ?Footprint selection? - ?Forest canopies? - ?Forest canopy height? - ?Global ecosystem dynamic investigation? - ?Height estimation? - ?Terrain slope? - ?Topographic slope? - ?Weak beams

Classification code: 481.1 Geology? - ?716.1 Information Theory and Signal Processing? - ?716.2 Radar Systems and Equipment? - ?741.3 Optical Devices and Systems? - ?821.1 Woodlands and Forestry? - ?1106.3 Digital Signal Processing? - ?1201 Mathematics? - ?1202 Statistical Methods? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 6.00E+01%, Size 1.50E+02m, Size 1.96E+02m

DOI: 10.6041/j.issn.1000-1298.2025.08.033

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

47. Detection of Early Decayed Citrus by Combining with Structured-illumination Reflectance Imaging and YOLO v7-CA

Accession number: 20253519059774

Title of translation: 基于结构光反射成像和 YOLO v7-CA 的柑橘早期腐烂检测方法

Authors: Cai, Zhonglei (1, 2); Shi, Ruiyao (1); Zhang, Junyi (1); Cai, Letian (1); Zhang, Yizhi (1); Zhang, Yawei (3); Li, Jiangbo (1)

Author affiliation: (1) Intelligent Equipment Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing; 100097, China; (2) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832003, China; (3) College of Engineering, 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: 8

Issue date: August 2025

Publication year: 2025

Pages: 479-486 and 495

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Early detection of decay caused by fungal infection in citrus fruit is a major challenge for the citrus industry, as the early decayed area is almost invisible on the surface of fruit. A detection system for structural illumination reflectance imaging was constructed based on light-emitting diode (LED) lamp and an effective methodology was proposed combining with spiral phase transform (SPT) algorithm for the early detection of decayed citrus. Three strategies (i. e. three-phase-shifting method, 2-phase SPT and 1-phase SPT) were used to demodulate original patterned images. The direct component (DC) and alternating component (AC) images were recovered by demodulating phase-shifting patterned images. Compared with the DC image, the early decayed area can be clearly displayed in the AC image and the ratio (i. e. AC/DC) image. YOLO v7 — CA model was proposed to address the issue of misjudgment between citrus decayed area and fruit stem. In actual testing, fruit stem can be misidentified as decayed area. The coordinate attention (CA) module was introduced in the backbone network of YOLO v7 model, in order to increase the attention to the decayed area. The improved YOLO v7 — CA model combined with RT images under 2-phase SPT achieved good detection results for early decayed area and fruit stem in citrus. Early decayed mandarins from different years were tested with a detection accuracy of 98. 5% . The research result can provide a reference for the detection of early decayed citrus. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Citrus fruits

Controlled terms: Light emitting diodes? - ?Optical variables measurement

Uncontrolled terms: Citrus? - ?Coordinate attention? - ?Early decay? - ?Patterned image? - ?Phase transform? - ?Reflectance imaging? - ?Spiral phase? - ?Structured illumination? - ?Structured-illumination reflecance imaging? - ?YOLO v7

Classification code: 714.2 Semiconductor Devices and Integrated Circuits? - ?821.5 Agricultural Products? - ?941.2 Optical Variables Measurements

Numerical data indexing: Percentage 5.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.08.045

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

48. Performance Evaluation and Optimal Design of Planar 3-RRR Parallel Manipulator Based on Geometric Algebra

Accession number: 20253519059800

Title of translation: 基于几何代数的平面 3-RRR 并联机构性能评估与优化设计

Authors: Chai, Xinxue (1); Zheng, Sunniao (1); Li, Yuanyuan (2, 3); Xu, Lingmin (1); Li, Qinchuan (1)

Author affiliation: (1) School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou; 310018, China; (2) State Key Laboratory oj High-end Heary-load Robots, Foshan; 528300, China; (3) Midea Corporate Research Center, Midea Group, Foshan; 528300, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 704-715

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to enable the planar 3 — RRR parallel manipulator (PM) to meet various working environments, the performance evaluation and optimal design of the 3 — RRR PM was conducted based on geometric algebra, where R denoted re volute joint. Firstly, the inverse kinematic model of the planar 3 — RRR PM was established by using the closed-loop vector method. Secondly, utilizing geometric algebra as a mathematical tool, the motion/force transmission performance of the 3 — RRR PM was analyzed in combination with local and global indices. And the transmission performance maps of the 3 — RRR PM were plotted at different positions and postures. Nextly, considering the compliances of limbs, a systemic elastostatic stiffness modeling of the planar 3 — RRR PM was presented based on the strain energy, which was verified by commercial ANSYS software in two cases. The local and global virtual-work stiffness indices were acquired at different external wrenches and positions. Based on the parameter-finiteness normalization method, the dimensional parameters of the 3 — RRR PM were optimized with the objectives of global motion/force transmission performance, stiffness performance and reachable workspace. Based on the above results, the influence of the key dimensional parameters on the manipulator performance was discussed, from which the optimal structures and performance chart of the planar 3 — RRR PM in different working environments can be obtained. The research results were of great significance for the prototype design of PMs. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Structural optimization

Controlled terms: Geometry? - ?Inverse kinematics? - ?Inverse problems? - ?Linear algebra? - ?Manipulators? - ?Optimal systems? - ?Stiffness? - ?Tools? - ?Transmissions

Uncontrolled terms: Force transmission? - ?Geometric Algebra? - ?Motion/force transmission index? - ?Optimal design? - ?Parallel manipulators? - ?Performances evaluation? - ?Stiffness index? - ?Transmission performance? - ?Virtual works? - ?Virtual-work stiffness index

Classification code: 214 Materials Science? - ?602.2 Mechanical Transmissions? - ?691.1 Materials Handling Equipment? - ?961 Systems Science? - ?1201 Mathematics? - ?1201.1 Algebra and Number Theory? - ?1201.7 Optimization Techniques? - ?1201.14 Geometry and Topology? - ?1301.1.1 Mechanics

DOI: 10.6041/j.issn.1000-1298.2025.08.066

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

49. Monitoring of Soil Salt Content during Different Growth Periods of Crops Based on Sentinel-1/2

Accession number: 20253519069960

Title of translation: 不同植被覆盖条件下Sentinel-1/2数据融合监测土壤含盐量模型研究

Authors: Dai, Tianjin (1, 2); Chen, Junying (1, 2); Guo, Jiaqi (1, 2); Bai, Xuqian (1, 2); Qian, Long (1, 2); Ba, Yalan (1, 2); Zhang, Zhitao (1, 2)

Author affiliation: (1) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Key Laboratory of Agricultural Soil and Water Engineering in Arid Areas, Northwest A&F University, Ministry of Education, Shaanxi, Yangling; 712100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 32-41

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurately and rapidly acquiring soil salinity content ( SSC ) information is crucial for agricultural sustainable development. Satellite remote sensing technology has attracted extensive attention in SSC monitoring due to its advantage of large-scale synchronous monitoring, but its monitoring accuracy often faces challenges from multiple error sources such as vegetation coverage interference and irrigation events. SSC under different vegetation coverage conditions was monitored based on Sentinel - 1/2 satellite data combined with ground-measured data, aiming to clarify the impact of different vegetation coverage on the accuracy of SSC remote sensing monitoring. Firstly, the full vegetation coverage period was divided into three stages (Dl ; early stage; D2; middle stage; D3 ; late stage) according to vegetation coverage, NDVI variation trends, and crop growth periods. Secondly, the sensitivity of variables (vegetation indices and polarization indices) to SSC at different soil depths was analyzed, and the variable importance in the projection ( VIP ) analysis algorithm was used for variable screening. Finally, machine learning algorithms ( support vector machine ( SVM ) , random forest ( RF) , and extreme learning machine (ELM) models) were integrated to generate SSC distribution maps for different soil depths in each stage. Results showed that variables had the highest correlation with SSC in D2 , followed by D3 and Dl. Fusion of radar and optical remote sensing data contributed to SSC monitoring across different crop stages. The RF model proved optimal for SSC monitoring, with the highest accuracy (R2 of 0. 79, RMSE of 1. 62 g/kg) at 10 ~ 20 cm soil depth. Spatially, the southern part of the study area exhibited the most severe soil salinization. Vertically, SSC was the highest at 20 ~ 40 cm across all stages. Temporally, SSC in 0 ~ 10 cm and 10 ~ 20 cm layers was increased with crop growth, while SSC at 20 ~ 40 cm showed a decreasing trend. These findings provided a scientific basis for precise monitoring and prevention of regional soil salinization. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Learning systems

Controlled terms: Agribusiness? - ?Crops? - ?Learning algorithms? - ?Optical remote sensing? - ?Soil surveys? - ?Support vector machines? - ?Vegetation

Uncontrolled terms: Crop growth? - ?Crop growth period? - ?Fractional vegetation cover? - ?Growth period? - ?Machine-learning? - ?Sentinel - 1/2? - ?Sentinel-1? - ?Soil salt content? - ?Variable importance in the projection? - ?Variable importances

Classification code: 103 Biology? - ?405.3 Surveying? - ?483.1 Soils and Soil Mechanics? - ?741.3 Optical Devices and Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?1101.2 Machine Learning

Numerical data indexing: Mass 6.20E-02kg, Size 0.00E00m to 1.00E-01m, Size 1.00E-01m to 2.00E-01m, Size 2.00E-01m to 4.00E-01m

DOI: 10.6041/j.issn.1000-1298.2025.08.003

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

50. Extraction of Rice-planting Structures under Terrain Heterogeneity by Fusing Multispectral Indices with Machine-learning Algorithms

Accession number: 20253519069921

Title of translation: 地形异质下光谱组合与机器学习融合的水稻种植结构提取研究

Authors: Ding, Ning (1); Li, Xiaomei (2); Sun, Jing (2); Wang, Shimei (2); Chen, Yang (2); Shi, Yuanzhi (1)

Author affiliation: (1) Nanjing Hydraulic Research Institute, Nanjing; 210029, China; (2) Xinjiang Raohe Hydrology and Water Resources Monitoring Center, Shangrao; 334000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 172-181 and 206

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurately capturing crop planting structures at regional scales is crucial for precise agricultural management, optimal water resource allocation, and food security assurance. Focusing on typical geomorphological units in Jiangxi Province, selecting the Xinfeng irrigation area with plain terrain and the Dashan irrigation area characterized by hilly terrain as case study areas. Utilizing Sentinel - 2 remote sensing data, a remote sensing feature matrix incorporating the normalized difference vegetation index ( NDVI ) , enhanced vegetation index ( EVI ) , modified normalized difference water index (MNDWI) , and their various combinations was constructed. Five machine learning algorithms, Naive Bayes (NB) , support vector machine ( SVM ) , multi-layer perceptron (MLP) , random forest (RF) , and extreme gradient boosting (XGBoost) , were coupled with this matrix. Cross-validation and grid search were implemented for optimizing model parameters, aiming to identify the best-performing combination of spectral indices and algorithms for extracting rice-planting structures. Additionally, data augmentation techniques such as translation and noise perturbation were introduced to simulate the impacts of inter-annual climate variability and crop growth temporal dynamics on classification accuracy. Rice-planting structures, including double-cropping rice, single-season late rice, and medium rice, were mapped for both irrigation areas. Results indicated that the combination of NDVI + MNDWI with the XGBoost algorithm achieved the highest classification performance in both irrigation areas. Specifically, Dashan exhibited an overall accuracy of 97. 30% and a Kappa coefficient of 0. 958 , whereas Xinfeng achieved an overall accuracy of 95. 40% and a Kappa coefficient of 0. 915. Both areas demonstrated average producer and user accuracies exceeding 95% . Terrain specificity, field conditions, and cropping complexity emerged as critical factors influencing the optimal spectral index and machine learning algorithm combination. Mapping results revealed clearly defined field boundaries and uniform shapes in the Xinfeng irrigation area, whereas Dashan featured irregular and fragmented fields with indistinct boundaries. Double-cropping rice dominated in Xinfeng, accounting for 80. 50% of the total area, while single-season rice predominated in Dashan, covering 78. 60% . This research established optimal remote sensing classification methods and spectral index combinations under varied terrain conditions, providing robust technical support and methodological guidance for extracting rice-planting structures at the irrigation-area scale and enhancing precision agricultural management. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Learning algorithms

Controlled terms: Agricultural machinery? - ?Classification (of information)? - ?Classifiers? - ?Computational complexity? - ?Crops? - ?Food supply? - ?Irrigation? - ?Landforms? - ?Learning systems? - ?Matrix algebra ? - ?Photomapping? - ?Resource allocation? - ?Support vector machines? - ?Vegetation? - ?Water resources

Uncontrolled terms: Agricultural management? - ?Irrigation area? - ?Irrigation districts? - ?Machine learning algorithms? - ?Normalized difference vegetation index? - ?Normalized difference water index? - ?Plantings? - ?Remote sensing classification? - ?Rice-planting structure? - ?Spectral indices

Classification code: 103 Biology? - ?405.3 Surveying? - ?444 Water Resources? - ?481.1 Geology? - ?716.1 Information Theory and Signal Processing? - ?731.1 Control Systems? - ?742.1 Photography? - ?802.1 Chemical Plants and Equipment? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?822.3 Food Products? - ?903.1 Information Sources and Analysis? - ?912.2 Management? - ?1101.2 Machine Learning? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1201.1 Algebra and Number Theory

Numerical data indexing: Percentage 3.00E+01%, Percentage 4.00E+01%, Percentage 5.00E+01%, Percentage 6.00E+01%, Percentage 9.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.016

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

51. Intelligent Decision Making for Maize Threshing Parameters in Multiple Operating Modes

Accession number: 20253519072440

Title of translation: 多种工况下玉米脱粒参数的智能决策

Authors: Dong, Jiaqi (1); Gui, Tao (1); Zhang, Dongxing (1); Yang, Li (1); He, Xiantao (1); Xing, Shunlun (1)

Author affiliation: (1) College of Engineering, 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: 8

Issue date: August 2025

Publication year: 2025

Pages: 314-319

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of serious grain loss and working parameters not being able to be adjusted in real time during the operation of maize combine harvester, a maize threshing parameter multi-mode intelligent decision-making system was designed, which can dynamically adjust the working parameters of the threshing device so as to reduce the harvest loss. The mechanism of threshing parameter regulation was explored. The entrainment loss monitoring system and threshing parameter intelligent regulation system were developed. The intelligent decision model of threshing parameters was constructed based on Simulink environment, and the proposed system was simulated and tested, and the results showed that the system could better achieve the set objectives in both high-efficiency harvesting mode and low-loss harvesting mode. The performance of the intelligent regulation system was verified through field tests, and the test results showed that the average value of the entrainment loss rate was 1. 767% in the low-loss harvesting mode, which was decreased by 16.536% relatively to the traditional mode; and the average value of the entrainment loss rate was 2. 030% in the high-efficiency harvesting mode, which was increased by 1. 970% relatively to the traditional mode, which proved that the proposed system performed well and met the requirements of actual harvesting operations. The research results can provide technical support for improving the intelligent level of maize combine harvester, and provide a reference for the improvement of maize crop harvesting yield and quality. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Efficiency

Controlled terms: Combines? - ?Decision making? - ?Grain (agricultural product)? - ?Harvesters? - ?Harvesting

Uncontrolled terms: Combine harvesters? - ?Entrainment loss? - ?Entrainment loss monitoring? - ?Intelligent decision making for threshing parameter? - ?Intelligent decision-making? - ?Intelligent regulations? - ?Maize? - ?Multiple operating mode? - ?Operating modes? - ?Working parameters

Classification code: 821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?912.2 Management? - ?913.1 Production Engineering

Numerical data indexing: Percentage 1.6536E+01%, Percentage 3.00E+01%, Percentage 7.67E+02%, Percentage 9.70E+02%

DOI: 10.6041/j.issn.1000-1298.2025.08.029

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

52. Tomato Fruit Recognition in Complex Scenes Based on FPBW-YOLO v8

Accession number: 20253519059769

Title of translation: 基于 FPBW-YOLO v8的复杂场景下番茄果实识别方法

Authors: Gu, Wenjuan (1); Liu, Haozhuang (1); Wei, Jin (1); Gao, Wenqi (1); Yin, Yanchao (1); Liu, Xiaobao (1)

Author affiliation: (1) Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming; 650500, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 467-478

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Rapid and accurate detection of tomato fruits is an important prerequisite of intelligent harvesting. Aiming at deployment requirements and problems of complex background, branch and leaf occlusion, and overlapping of fruits, an improved detection method based on improved YOLO v8n was proposed. Firstly, FasterNet was selected as the backbone feature extraction network to improve the feature extraction capability of the model. Nextly, the small target detection layer and bidirectional feature pyramid network (BiFPN) structure were fused in the neck network, reducing the interference of the complex background to improve the detection accuracy of the model. Subsequently, the wise intersection over union (WIoU) loss function was used to improve the detection accuracy of the model in the presence of occlusion and overlap, enhancing the model’s convergence ability. Finally, the model was deployed to a mobile terminal based on the NCNN framework. Experimental results on the tomato public dataset showed that the precision, recall, mAP@ 0. 5 and mAP@ 0. 5:0. 95 of the FPBW - YOLO v8 model reached 97. 9%, 95. 1%, 98. 3% and 74. 3%, respectively, all of which were higher than the results of Faster R-CNN, SSD, YOLO v8n, YOLO v7, YOLO v5n and Rt - Detr. The model brought forward in this research could obtain high detection accuracy on hardware devices with limited computational resources, which can effectively solve the problem of tomato fruit recognition in complex scenes and provide technical support for tomato picking robots. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Fruits

Controlled terms: Complex networks? - ?Extraction? - ?Feature extraction? - ?Object recognition? - ?Plants (botany)

Uncontrolled terms: Bidirectional feature pyramid network? - ?Detection accuracy? - ?Fasternet? - ?Feature pyramid? - ?Fruit recognition? - ?Improved YOLO v8n? - ?Objects detection? - ?Pyramid network? - ?Tomato fruits? - ?Wise intersection over union

Classification code: 103 Biology? - ?802.3 Chemical Operations? - ?821.5 Agricultural Products? - ?1101.2 Machine Learning? - ?1105 Computer Networks? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 1.00E00%, Percentage 3.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.08.044

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

53. Design and Experiment of Seedbed Construction Device for Wheat Compound Seeder

Accession number: 20253519069916

Title of translation: 小麦复式播种机种床构建装置设计与试验

Authors: He, Ruiyin (1, 2); Liu, Xiaofeng (1, 2); Li, Yinian (1, 2); Ding, Qishuo (1, 2); Meng, Weiguo (1, 2); Xu, Gaoming (3); Sun, Jianfu (1, 2)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 210031, China; (2) Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing; 210031, China; (3) Jiangmen Polytechnic, Jiangmen; 529090, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 239-251

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The low seedbed quality and the poor furrow construction was a major problem constraining wheat production in the lower reaches of the Yangtze River region. To address this problem, a technical process featured with “rotary tilling + furrow opening + pre-seeding compaction + furrow stabilizing” was proposed and a key functional component was designed. The key part of the seeder was its assemblage of a rotor tiller, a set of moldboard plough furrow opener, and a pre-seeding compacting roller. This design facilitated one-pass seeding operation which included rotary tilling, furrow opening, pre-seeding compaction, and starter fertilizing etc. The combined usage of both the pressure-adjustable pre-seeding compacting roller and the trans-directional soil transporting technology guaranteed a levelled and well loose-compact mediated seedbed for optimal wheat germination. With respect to the agronomic specifications for wheat cultivation, suitable number of the rotary knives and their arrangement on the rotor was determined and the key parameters of both the moldboard plough furrow opener and the pre-seeding compacting roller were optimized. EDEM simulating program was used for 2-factor and 4-level orthogonal experiment and the simulated results were analyzed and plotted with Box - Behnken method, from which the terminal angle ( i. e. 68°) of the moldboard plough furrow opener and the optimal horizontal tilting angle (i. e. 67°) for the side plate of the furrow stabilizer were determined. The whole-system optimization of the one-pass seeder included a forwarding speed of 4. 22 km/h, a rotational speed of 288 r/min for the rotary tiller and a 85. 50 mm workable range for the hydraulic cylinder. The experiment was carried out in the field with soil moisture content of 22. 3% and soil bulk density of 1.11 g/3 , the results showed that post-seeding soil fragmentation was 92. 75% , soil micro-relief was 15. 32 mm, soil volumetric weight was 1. 34 g/3 , mean furrow depth was 201 mm and the furrow depth uniformity was 91. 38% . Wheat seedling emergence was uniform, with a germination rate of 89. 24% , meeting the agronomic requirement for wheat cultivation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Compaction

Controlled terms: Agricultural machinery? - ?Agronomy? - ?Cultivation? - ?Hydraulic machinery? - ?Molds? - ?Plants (botany)? - ?Rollers (machine components)? - ?Seed? - ?Soil moisture

Uncontrolled terms: Furrow openers? - ?Mold plow opener? - ?Moldboard plows? - ?One-pass? - ?Pre-sowing repression? - ?Seedbed construction? - ?Technical process? - ?Wheat compound seeder? - ?Wheat production? - ?Yangtze River

Classification code: 103 Biology? - ?483.1 Soils and Soil Mechanics? - ?601.2 Machine Components? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?913.4 Manufacturing? - ?1401.2 Hydraulic Equipment and Machinery

Numerical data indexing: Angular velocity 4.8096E+00rad/s, Mass 1.11E-03kg, Mass 3.40E-02kg, Percentage 2.40E+01%, Percentage 3.00E+00%, Percentage 3.80E+01%, Percentage 7.50E+01%, Size 2.01E-01m, Size 2.20E+04m, Size 3.20E-02m, Size 5.00E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.022

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

54. Spatiotemporal Dynamics Monitoring of Drought in Yangtze River Basin Using Temperature-Precipitation-Vegetation Integrated Dryness Index (TPVDI)

Accession number: 20253519069886

Title of translation: 基于温度-降水-植被综合干旱指数的长江流域干旱时空动态监测

Authors: Jiang, Hao (1); Zhang, Hanbo (2); Liu, Chengbin (1); Huang, He (3); Yang, Junxing (3); Fu, Tianxin (4)

Author affiliation: (1) School of Water Conservancy and Civil Engineering, Beijing Vocational College of Agriculture, Beijing; 102442, China; (2) Cuangxi Forest Inventory and Planing Institute, Nanning; 530000, China; (3) School of Ceomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing; 102616, China; (4) School of Smart Agricultural Engineering, Beijing Vocational College of Agriculture, Beijing; 102442, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 95-106 and 119

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Under intensifying global warming, drought events have become increasingly severe and frequent. Accurate assessment of drought severity, efficient monitoring of drought distribution characteristics, and investigation of spatiotemporal evolution patterns are critical for timely drought management and the formulation of evidence-based mitigation strategies. Focusing on the Yangtze River Basin, the temperature - precipitation - vegetation dryness index ( TPVDI) , a comprehensive drought index derived from the spatial Euclidean distance integration of the temperature condition index (TCI) , precipitation condition index (PCI) , and vegetation condition index (VCI) was innovatively developed. The coefficient of variation ( CV ) method, Theil - Sen Median trend analysis coupled with Mann - Kendall significance testing, and Hurst exponent analysis were systematically applied to reveal the spatiotemporal dynamics of drought from 2001 to 2019. Key findings included that TPVDI demonstrated robust performance in monitoring meteorological, agricultural, and comprehensive droughts, showing significant correlations with SPEI - 12, TVDI, and PA ( -0.491 6, 0.429 9, and - 0.398 5, respectively; all statistically significant at p ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Global warming

Controlled terms: Data integration? - ?Drought? - ?Forestry? - ?Rivers? - ?Vegetation? - ?Watersheds? - ?Wetting

Uncontrolled terms: Condition index? - ?Distribution characteristics? - ?Drought dynamic monitoring? - ?Drought severity? - ?Dynamic monitoring? - ?Efficient monitoring? - ?Integrated drought index? - ?Spatio-temporal dynamics? - ?Temperature - precipitation - vegetation dryness index? - ?Yangtze River basin

Classification code: 103 Biology? - ?407 Maritime and Port Structures; Rivers and Other Waterways? - ?443.1 Atmospheric Properties? - ?443.3 Precipitation? - ?444 Water Resources? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.1 Woodlands and Forestry? - ?1106.2 Data Handling and Data Processing? - ?1301.1.2 Physical Properties of Gases, Liquids and Solids? - ?1502.1.2 Climate Change? - ?1502.3 Hydrology

Numerical data indexing: Age 1.90E+01yr, Age 3.00E+00yr to 5.00E+00yr, Percentage 1.625E+01%, Percentage 1.80E+01%, Percentage 2.20E+01%, Percentage 5.50E+01%, Percentage 8.143E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.009

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

55. Impact of Blade Passage Flow Distortions on Runner Forces in Pump-turbine under Different Guide Vane Openings

Accession number: 20253519072253

Title of translation: 不同开度下水泵水轮机叶道内流畸变对转轮的受力影响研究

Authors: Li, Qifei (1, 2); Chen, Xiangyu (1, 3); Xin, Lu (1, 2); Zhang, Shiang (4)

Author affiliation: (1) School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; (2) Gansu Key Laboratory of Fluid Machinery and Systems, Lanzhou; 730050, China; (3) Jiuquan Vocational Technical University, Jiuquan; 735000, China; (4) Tianjin Tianfa Heavy Hydroelectric Equipment Manufacturing Co., Ltd., Tianjin; 300400, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 333-340

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The variation of guide vane opening in pump-turbines induces internal flow distortions within the runner, significantly influencing hydraulic performance and structural integrity. To investigate the impact of such flow distortions on runner dynamics, a combined experimental and numerical approach was employed, focusing on a model pump-turbine from a domestic pumped-storage power station. Utilizing the SST k-ω turbulence model, three-dimensional unsteady numerical simulations were conducted across the full flow passage under different guide vane openings. Aiming to elucidate the relationship between guide vane-induced flow distortions and the resultant hydrodynamic forces on the runner blades and radial loads.The findings revealed that as the guide vane opening increased, the internal flow velocity within the runner passage was escalated, with the maximum velocity localized near the pressure side of the blades. Notably, each blade passage exhibited a distinct vortex structure, with the vortex core positioned at approximately two-thirds of the blade’s relative length. These vortices, arising from flow separation and secondary flows, critically influenced the pressure distribution on the runner blades. Specifically, the minimum pressure on the PS and the maximum pressure on the suction side consistently occurred near the vortex core, highlighting the role of flow distortions in dictating blade loading patterns. Furthermore, the unsteady flow distortions exerted a systematic impact on the radial forces acting on the runner. The resultant radial force exhibited periodic fluctuations, with the number of peaks and troughs corresponding to the number of runner blades. This periodicity aligned with the spatial distribution and temporal evolution of the vortices within the blade passages, underscoring the coherence between flow instabilities and mechanical excitations. The study demonstrated that the runner’s radial force characteristics were intrinsically linked to the dynamic behavior of the vortical structures, which were modulated by the guide vane opening. These insights contributed to a deeper understanding of the fluid-structure interaction mechanisms in pump-turbines under off-design conditions. The results emphasized the necessity of optimizing guide vane control strategies to mitigate adverse flow distortions, thereby enhancing operational stability and fatigue resistance. The methodology and conclusions presented herein can provide a foundation for further investigations into transient hydraulic phenomena in reversible pump-turbines, with implications for design and condition monitoring in pumped-storage systems. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 20

Main heading: Flow separation

Controlled terms: Computational fluid dynamics? - ?Fatigue of materials? - ?Flow velocity? - ?Hydraulic machinery? - ?Hydraulic motors? - ?Hydraulic turbines? - ?Hydraulics? - ?Pumped storage power plants? - ?Structural design? - ?Turbine components ? - ?Turbine pumps? - ?Vortex flow? - ?Vorticity

Uncontrolled terms: Blade passage? - ?Flow distortion? - ?Guide vane openings? - ?Guide-vane? - ?Internal flow distortion? - ?Internal flows? - ?Opening? - ?Pump-turbines? - ?Radial forces? - ?Runner stress

Classification code: 214 Materials Science? - ?301.1 Fluid Flow? - ?301.1.3 Aerodynamics (fluid flow)? - ?301.1.4 Computational Fluid Dynamics? - ?408 Structural Design? - ?609.2 Pumps? - ?651 Aerodynamics? - ?941.5 Mechanical Variables Measurements? - ?1007.1 Turbines and Steam Turbines? - ?1008.1.1 Hydroelectric Power Plants? - ?1401.1 Hydraulics? - ?1401.2 Hydraulic Equipment and Machinery

DOI: 10.6041/j.issn.1000-1298.2025.08.031

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

56. Phenological Driving Mechanism and Time-delay Cumulative Effect of Vegetation in Arid and Semi-arid Regions Based on Random Forest

Accession number: 20253519069940

Title of translation: 基于随机森林算法的干旱半干旱区植被物候驱动 机制与时滞累积效应研究

Authors: Li, Ruiping (1, 2); Wang, Ying (1); Zheng, Hexiang (3); Qin, Ziyuan (3); Hou, Hongfei (3)

Author affiliation: (1) College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China; (2) State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot; 010020, China; (3) Institute of Water Conservancy Science, Ministry of Water Resources, Pastoral Area, Hohhot; 010020, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 86-94

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Vegetation phenology is a key ecological indicator to characterize the response of vegetation to climate change, and its dynamic changes have important effects on ecosystem stability in arid regions. To further reveal the response mechanism of vegetation onset period (SOS) to multiple climatic factors in arid and semi-arid areas, a comprehensive evaluation system combining random forest ( RF) model, SHAP interpretation algorithm and time delay accumulation analysis was constructed based on NDVI data, multi-source climate data and SOS data from 2001 to 2020. The results showed that the pre-season length of diurnal temperature range (DTR) , daily mean temperature (Tmean) , cloud cover (CLD) and monthly total precipitation (Pre) were concentrated in 3 ~ 4 months (55.6% region) , 1 ~3 months (60.3% region) , 4 ~6 months (58. 1% region) and 4 ~6 months (62. 9% region) , respectively. Pre was the most critical driver of SOS, while CLD, DTR and T’ n provided complementary regulation in specific situations. The response characteristics of different types of climate factors were significantly different, among which the thermal factors such as Tmean and DTR mainly had a lag of 1 ~ 3 months and no cumulative effect. The response mechanism of water factors such as Pre, surface soil moisture ( SM ) and standardized precipitation evapotranspiration index ( SPEI) was mainly the current month and short-term accumulation. CLD was mainly reflected in the regulation effect of two months lag and one month accumulation. The results revealed the response of vegetation phenology to multiple climatic factors in arid and semi-arid areas, which provided theoretical basis and method support for the construction of climate - vegetation interaction models at regional scale, the optimization of agricultural production layout, and the prediction of vegetation dynamics under climate change. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Climate change

Controlled terms: Abiotic? - ?Agricultural robots? - ?Agriculture? - ?Arid regions? - ?Climate models? - ?Ecosystems? - ?Operations research? - ?Precipitation (chemical)? - ?Precipitation (meteorology)? - ?Soil moisture ? - ?Time delay? - ?Timing circuits? - ?Vegetation

Uncontrolled terms: Cloud cover? - ?Cumulative effects? - ?Diurnal temperature ranges? - ?Mu us sandy land? - ?Random forests? - ?Response mechanisms? - ?SHAP analyze? - ?Time-delay cumulative effect? - ?Time-delays? - ?Vegetation phenology

Classification code: 103 Biology? - ?443 Meteorology? - ?443.3 Precipitation? - ?444 Water Resources? - ?483.1 Soils and Soil Mechanics? - ?713 Electronic Circuits? - ?713.4 Pulse Circuits? - ?731.6 Robot Applications? - ?802.3 Chemical Operations? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?1502.1.2 Climate Change? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Age 2.499E-01yr to 3.332E-01yr, Age 3.332E-01yr to 4.998E-01yr, Age 8.33E-02yr to 2.499E-01yr, Percentage 1.00E00%, Percentage 5.56E+01%, Percentage 6.03E+01%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.08.008

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

57. Greenhouse Robot Localization and Dense Map Building Method Based on Improved ORB-SLAM2

Accession number: 20253519059706

Title of translation: 基于改进 ORB-SLAM2 算法的温室机器人定位与稠密建图方法

Authors: Li, Xu (1, 2); Yang, Aokai (1); Liu, Qing (1); Wu, Bei (1, 2); Ji, Bang (1, 2); Liu, Dawei (3, 4); Xie, Fangping (3, 4)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha; 410128, China; (2) Hunan Key Laboratory of Intelligent Agricultural Machinery Equipment, Changsha; 410128, China; (3) Key Laboratory of Intelligent Seedling Cultivation, Ministry of Agriculture and Rural Affairs, Yiyang; 413055, China; (4) Hunan Research Center of Engineering Technology for Intelligent Seedling Equipment, Yiyang; 413055, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 427-437

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problem that the environment inside the greenhouse is complieated and ORB -SLAM2 cannot build dense maps, the research on the greenhouse robot localization and dense map building method was carried out based on the improved ORB — SLAM2. Firstly, a feature point extraction threshold method was proposed in the tracking thread to adaptively adjust the feature point extraction threshold according to the overall pixels of the image to improve the quality and quantity of feature point extraction. Secondly, on the basis of ORB — SLAM2, combined with the relative position calculation between frames, the amount of rotation and translation was added as the key frame selection condition, which reduced the number of key frames and the average tracking time, and improved the positioning accuracy. Finally, a dense map building thread was introduced to generate fine 3D dense maps by fusing multi-frame point cloud data through point cloud recovery, statistical filtering, point cloud stitching and voxel filtering algorithms. In order to verify the effectiveness and practicality of the method, simulation analysis of public datasets and real scenario tests were conducted, respectively, and the improved algorithm was closer to the real trajectory than the ORB — SLAM2 running trajectory on the Freiburgl_ room, Freiburgl _xyz, and Freiburgl _desk sequences, and the average absolute trajectory error was reduced by 46. 00%, 29. 01%, 39. 85%, respectively. Within the greenhouse environments with three different branch and leaf shading, the improved algorithm improved the number of feature point matched by an average of 7. 20%, 12. 37%, and 12. 81% each compared with ORB — SLAM2; meanwhile, the average number of keyframes was reduced from 400, 525, and 1,132 frames to 371, 411, and 708 frames, respectively, and the average tracking time was reduced from 0. 039 0 s, 0. 035 7 s, 0. 031 8 s to 0.037 3, 0.034 3, and 0.029 0 s, respectively. The experimental results showed that the estimated trajectory of the improved algorithm basically fit with the actual trajectory of the greenhouse robot, which had good loopback detection performance, and a three-dimensional dense point cloud map of the greenhouse scene was successfully constructed, which accurately restored the real distribution of the crops and aisles in the three-dimensional space. This method can provide technical support for the localization and navigation of greenhouse mobile robots. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Trajectories

Controlled terms: Extraction? - ?Feature extraction? - ?Greenhouses? - ?Robot applications? - ?SLAM robotics? - ?Statistical tests

Uncontrolled terms: Adaptive thresholds? - ?Dense map building? - ?Feature points extraction? - ?Greenhouse robot? - ?Improved * algorithm? - ?Key frame selection? - ?Map Building? - ?ORB — SLAM2? - ?Point-clouds? - ?Robot localization

Classification code: 656 Space Flight and Research? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?802.3 Chemical Operations? - ?821.7 Farm Buildings and Other Structures? - ?1101.2 Machine Learning? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 0.00E00%, Percentage 1.00E00%, Percentage 2.00E+01%, Percentage 3.70E+01%, Percentage 8.10E+01%, Percentage 8.50E+01%, Time 0.00E00s, Time 7.00E+00s, Time 8.00E+00s to 3.70E-02s

DOI: 10.6041/j.issn.1000-1298.2025.08.040

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

58. Design and Experiment of High-precision Semi-open Pig Performance Testing Station Based on Multi-modal Fusion Technology

Accession number: 20253519059791

Title of translation: 基于多模态融合技术的高精度半开放式种猪性能测定站设计与试验

Authors: Li, Xuan (1, 2); Liang, Hao (2); Liu, Xiaolei (3); Li, Mao (2); Xu, Dihong (1, 2); Zeng, Rong (1, 2)

Author affiliation: (1) Key Laboratory of Smart Farming for Agricultural Animals, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (2) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (3) Hubei Hongshan Laboratory, Wuhan; 430070, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 507-516

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to improve the automation level of pig performance measurement and reduce the influence of external interference on the measurement accuracy, a high-precision semi-open pig performance testing station based on multi-modal fusion technology was designed. Employing the modular design principle, the structure of the testing station was devised. It was partitioned into two modules; the feeding end and the body-weight weighing end. Leveraging the STM32 main control chip and the FreeRTOS operating system, the hardware and software of the control system were constructed to operate in tandem with the image acquisition equipment. A weighing anomaly detection algorithm based on multimodal fusion was proposed. This approach employed background subtraction and color filtering techniques to pinpoint anomalies in the designated area. Concurrently, it integrated the variance and range of weighing signals for dynamic analysis and optimization of the time window, which effectively filtered out the noise in the weighing data. The test of the actual pig herd was carried out, and the results showed that after the weighing data was denoised, 78. 39% of the abnormal data of No. 1 pig house and 76. 68% of the abnormal data of No. 2 pig house were restored to normal data. The results showed that in the 30 ~ 60 kg and 30 ~ 100 kg stages, the average daily ad libitum feeding times of the experimental herd were 5. 30 (No. 1 pig house), 5. 29 (No. 2 pig house), 5. 92 (No. 1 pig house) and 5. 90 (No. 2 pig house), respectively. The average daily feeding time was 58.43 min (No. 1 pig house), 63.23 min (No. 2 pig house), 52. 01 min (No. 1 pig house) and 54. 95 min (No. 2 pig house), respectively. As for the feed-to-meat ratios (FCR), it was 2. 58 (No. 1 pig house) and 2. 57 (No. 2 pig house) for 30 ~ 60 kg stage, 2. 82 (No. 1 pig house) and 2. 75 (No. 2 pig house) for 30 ~ 100 kg stage. Moreover, the fitting growth curves at the age of 80 ~ 140 d were in line with the Logistic model, which was consistent with the growth patterns of the herd. The research findings indicated that this testing station was capable of reliably evaluating the performance of breeding pigs. It offered substantial support for precision breeding and modern swine production practices, which had important application value and promotion significance for improving the efficiency of breeding pigs selection and optimizing the genetic improvement of breeding pigs. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Feeding

Controlled terms: Agriculture? - ?Anomaly detection? - ?Anthropometry? - ?Filtration? - ?Houses? - ?Image acquisition? - ?Mammals? - ?Modal analysis? - ?Signal analysis? - ?Signal detection ? - ?Weighing

Uncontrolled terms: Breeding pig? - ?Electrical signal? - ?Fusion technology? - ?High-precision? - ?Image? - ?Multi-modal fusion? - ?Performance testing? - ?Performances evaluation? - ?Pig house? - ?Semi-open style

Classification code: 101.4 Biomechanics, Bionics and Biomimetics? - ?103 Biology? - ?402.3 Residences? - ?691.2 Materials Handling Methods? - ?716.1 Information Theory and Signal Processing? - ?802.3 Chemical Operations? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?942.1.7 Special Purpose Instruments? - ?1106 Computer Software, Data Handling and Applications? - ?1106.6 Data Analytics? - ?1201.5 Computational Mathematics

Numerical data indexing: Mass 3.00E+01kg to 1.00E+02kg, Mass 3.00E+01kg to 6.00E+01kg, Percentage 3.90E+01%, Percentage 6.80E+01%, Time 3.5058E+03s, Time 3.7938E+03s, Time 5.70E+03s, Time 6.00E+01s

DOI: 10.6041/j.issn.1000-1298.2025.08.048

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

59. Intelligent Identification Method of “Four Chaos” Based on YOLO v8n-SPE-SL

Accession number: 20253519069929

Title of translation: 基于YOLO v8n - SPE - SL的河道”四乱”识别方法

Authors: Liu, Ling (1); Ma, Xiaoyan (1); Sun, Tianyue (1); Shen, Xiaojun (2); Liu, Dongmei (3); Su, Changfa (3)

Author affiliation: (1) College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin; 300392, China; (2) College of Water Conservancy Engineering, Tianjin Agricultural University, Tianjin; 300392, China; (3) The Water Affairs Bureau of Jizhou District, Tianjin, Tianjin; 301900, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 163-171

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to solve the problems of small target size and complex background in the river channel “four chaos” detection image, an improved YOLO v8n - SPE - SL ( Small SPD - Conv - ECA SiLuan ) model was proposed to quickly and accurately identify the “four chaos” targets in the river channel. By adding a small target detection layer, the difficult problem of small target recognition in the river was solved. By introducing the SPD - Conv module to replace the partial convolution with step size of 2 in the original model, the loss of detail information was reduced. By adding the efficient channel attention (ECA) mechanism to some C2f modules, the ability to recognize small targets of the “four chaos” in the river was improved, and on this basis, the “four chaos” patrol system of the river was designed. Based on the constructed dataset, the results showed that the average accuracy, recall rate and average accuracy of the YOLO v8n - SPE - SL model reached 96. 3% , 91. 9% and 95. 7% , which were improved by 1 , 2. 5 and 1. 6 percentage points respectively compared with that of the YOLO v8n model. The introduction of the small target detection layer improved the mAP@50 by 0.7 percentage points, the SPD - Conv module reduced the false detection rate by 23. 6% , and the ECA mechanism increased the mAP@ 50 -95 by 2. 7 percentage points. The inspection system can be used to achieve precise identification and display of the four “chaos” phenomena (”unauthorized occupation,” “illegal construction,” “random piling,” and “illegal mining”) within river management areas, contributing to the construction of happy rivers and lakes. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Deep learning

Controlled terms: Chaos theory? - ?Crime? - ?Image enhancement? - ?River control? - ?Target tracking

Uncontrolled terms: Attention mechanisms? - ?Deep learning? - ?Efficient channels? - ?Four chaos? - ?Percentage points? - ?River channels? - ?River inspection? - ?Small target detection? - ?Small targets? - ?YOLO v8n

Classification code: 407 Maritime and Port Structures; Rivers and Other Waterways? - ?435.2 Tracking and Positioning? - ?442 Flood Control; Land Reclamation? - ?961 Systems Science? - ?971 Social Sciences? - ?1101.2.1 Deep Learning? - ?1106.3.1 Image Processing? - ?1201.4 Applied Mathematics

Numerical data indexing: Percentage 3.00E+00%, Percentage 6.00E+00%, Percentage 7.00E+00%, Percentage 9.00E+00%, Size 5.08E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.015

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

60. Joint Speed Slip Ratio Control Algorithm for Rice Transplanters Based on Paddy Field Rolling Resistance Variation Rate Identification and Fuzzy PI Control

Accession number: 20253519069928

Title of translation: 基于水田滚动阻力变化率辨识和模糊PI控制的 水稻插秧机速度-滑转率联合控制算法

Authors: Ma, Yueqi (1, 2); Chi, Ruijuan (1, 2); Fu, Guohui (1, 2); Zhao, Yantao (1, 2); Ban, Chao (1, 2); Su, Tong (1, 2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Vehicle Intelligent Control Laboratory, 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: 8

Issue date: August 2025

Publication year: 2025

Pages: 274-282

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The slip ratio of the drive wheels during rice transplanter operations affects the precision of planting spacing and subsequently impacts rice yield. Therefore, a joint speed - slip ratio control method was proposed based on paddy field rolling resistance variation rate identification and fuzzy PI control. Firstly, Kaiman filter was used to estimate the variation rate of rolling resistance of the transplanter’s drive wheel, which reflected the fluctuation intensity of the paddy field condition parameters. Subsequently, fuzzy PI control was used for joint speed - slip ratio control, where the fuzzy rules adjusted the control law gain coefficients in real-time based on the rolling resistance variation rate, speed error, and slip ratio error. Field test results indicated that, with a target speed of lm/s, the proposed controller achieved a mean slip ratio of 0. 122 with a variance of 0. 039, compared with the case without slip ratio control, the mean slip ratio was decreased by 39. 3% , the variance of slip ratio was decreased by 61. 7% , and compared with the fixed-control-law-gain-coefficients speed - slip ratio joint controller, the proposed method reduced the mean slip ratio by 12. 8% , the variance of the slip ratio by 45. 1% ; in terms of speed control, when the desired speed was 1 m/s, the proposed algorithm resulted in an absolute speed error mean of 0. 072 m/s, compared with the case without slip ratio control, it represented a 53. 5% reduction, and compared with the fixed-control-law-gain-coefficients speed - slip ratio joint controller, it represented a 47. 1% reduction. The proposed joint speed - slip ratio control method demonstrated significant improvements in reducing and stabilising the slip ratio of the transplanter’s drive wheels, showing effectiveness and strong robustness. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Controllers

Controlled terms: Control theory? - ?Errors? - ?Fuzzy filters? - ?Parameter estimation? - ?Plants (botany)? - ?Rolling resistance? - ?Three term control systems? - ?Two term control systems? - ?Wheels

Uncontrolled terms: Control laws? - ?Fuzzy PI control? - ?Kaiman filter? - ?Paddy fields? - ?Parameters identification? - ?Ratio control? - ?Resistance variations? - ?Rice transplanter? - ?Slip ratio? - ?Slip ratio control

Classification code: 103 Biology? - ?212.3 Rubber Products? - ?601.2 Machine Components? - ?731 Automatic Control Principles and Applications? - ?731.1 Control Systems? - ?731.1.1 Error Handling? - ?732.1 Control Equipment? - ?1101 Artificial Intelligence? - ?1201 Mathematics? - ?1202 Statistical Methods

Numerical data indexing: Percentage 1.00E00%, Percentage 3.00E+00%, Percentage 5.00E+00%, Percentage 7.00E+00%, Percentage 8.00E+00%, Velocity 1.00E00m/s, Velocity 7.20E+01m/s

DOI: 10.6041/j.issn.1000-1298.2025.08.025

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

61. ReID for Spraying Workers in Facility Pitaya Orchard Based on Gait-AVG

Accession number: 20253519059702

Title of translation: 基于 Gait-AVG 的设施火龙果园喷施作业人员重识别方法

Authors: Pu, Liuru (1, 2); Zhao, Yongjie (1, 2); Yang, Guangyuan (1, 2); Song, Huaibo (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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 438-446

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The agricultural workers are the core of intelligent supervision in facility agriculture. Using pesticide spraying operations as an example, the challenge of supervising personnel in complex environments such as greenhouse facilities was addressed. The focus was on personnel who use backpack sprayers for spraying operations in facility pitaya orchards, and a re-identification (ReID) method for facility pitaya orchard spraying personnel based on Gait — AVG was proposed. Multi-scale image feature extraction based on ResNet was achieved by the model, resulting in diverse features after undergoing temporal pooling and horizontal pooling pyramid. To bolster the efficacy of RelD in discerning sprayers amidst complex orchard settings, a mean pooling feature fusion technique was introduced. This method not only mitigated computational overhead but also leveraged multi-scale information to yield superior performance outcomes. Leveraging two distinct loss functions, namely Triplet Loss and Cross Entropy Loss, the training model was synthesized to bolster the monitoring and generalization capabilities pertinent to spraying behavior recognition. In order to substantiate the efficacy of the proposed methodology, a comprehensive facility environment spraying behavior dataset was curated, ensuring consistency in sample features and effective classification. The experimental evaluation of the proposed methodology on the CASIA — B dataset demonstrated compelling performance metrics; average Rank — 1 accuracies of 96.55%, 92. 19%, and 79.47% were attained for normal walking (NM), walking with backpack (BG), and walking with coat (CL) tasks, respectively. Notably, the proposed sprayer RelD method was validated in a production orchard, achieving a recognition accuracy of 91. 49% . Furthermore, robustness tests under occlusion, variation in shooting angle, and diverse lighting conditions yield recognition accuracies of 78. 06%, 97. 50%, and 96. 00%, respectively. The results indicated that this method can be used to identify and track personnel involved in spraying operations within facility environments. This study could effectively enhance the production efficiency of facility pitaya orchards and provide technical references for intelligent supervision of pitaya orchards. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Orchards

Controlled terms: Agricultural implements? - ?Agricultural machinery? - ?Computer vision? - ?Facilities? - ?Feature extraction? - ?Gait analysis? - ?Neural networks? - ?Personnel? - ?Walking aids

Uncontrolled terms: Agricultural workers? - ?Facilitated pitaya orchard? - ?Facility agricultures? - ?Gait - AVG? - ?Gait recognition? - ?Intelligent supervision? - ?Machine-vision? - ?Re identifications? - ?Recognition accuracy? - ?Workers’

Classification code: 101.1 Biomedical Engineering? - ?101.2.1 Hospital Equipment and Supplies? - ?101.4 Biomechanics, Bionics and Biomimetics? - ?101.6 Rehabilitation Engineering and Assistive Technology? - ?409 Civil Engineering, Other Topics? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?912.3 Personnel? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 0.00E00%, Percentage 1.90E+01%, Percentage 4.90E+01%, Percentage 5.00E+01%, Percentage 6.00E+00%, Percentage 7.947E+01%, Percentage 9.655E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.041

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

62. Kinematic Analysis of Rigid-Flexible Coupled Supernumerary Robotic Limbs and Human Robot Collaborative Control Research

Accession number: 20253519059784

Title of translation: 刚柔耦合外肢体机器人运动学与人机协作控制研究

Authors: Qi, Fei (1); Ge, Yiwei (1); Zhang, Heng (1); Liu, Xianjun (1); Sun, Jie (1); Li, Xiaoling (1); Chen, Bai (2)

Author affiliation: (1) School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou; 213164, China; (2) College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing; 210016, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 726-735 and 744

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of low control accuracy, human-machine motion incompatibility and poor collaborative effectiveness of rigid — flexible coupled rope-driven supernumerary robotic limbs under complex aggregate interference, an inertial sensor-based human upper limb motion intention recognition and collaborative control method was studied. Based on the D — H method and joint coupling mechanism, an active decoupling kinematic model of rope-driven supernumerary robotic limbs was constructed, an inertial sensor-based human upper limb motion capture system was designed to realize the dynamic collection of upper limb motion data, a neural network-based human upper limb motion recognition model was established, and a human - robot collaborative control strategy based on the coupled kinematic model and the upper limb motion intention was proposed. Finally, a single-arm rope-driven experimental prototype of supernumerary robotic limbs was built to validate the proposed upper limb motion recognition method and human — robot collaborative control strategy. The results showed that the accuracy of human motion intention recognition in eight groups of different morphologies was as high as 99. 75%, while the mean value of the end position deviation of the supernumerary robotic limbs was 4. 8 mm, and the maximum value of the position deviation was 6. 5 mm, which verified the feasibility and correctness of the proposed method of human motion recognition and human — robot collaborative control strategy, and ensured high-performance safety control and human — robot collaborative effectiveness. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Inertial navigation systems

Controlled terms: Collaborative robots? - ?Couplings? - ?Joints (anatomy)? - ?Kinematics? - ?Man machine systems? - ?Motion capture? - ?Motion estimation? - ?Motion sensors? - ?Robotic arms? - ?Rope

Uncontrolled terms: Collaborative control? - ?Control strategies? - ?Human -robot collaborative control? - ?Human motion intent recognition? - ?Human motions? - ?Human robots? - ?Inertial sensor? - ?Intent recognition? - ?Supernumerary robotic limb? - ?Upper limb motion

Classification code: 101.4 Biomechanics, Bionics and Biomimetics? - ?101.6.1 Robotic Assistants? - ?213.4 Fiber Products? - ?435.1 Navigation? - ?601.2 Machine Components? - ?602.1 Mechanical Drives? - ?709 Electrical Engineering, Other Topics? - ?731.5 Robotics? - ?942.1.4 Electric and Electronic Instruments? - ?1106.3.1 Image Processing? - ?1107 Human-Machine Systems? - ?1301.1.1 Mechanics

Numerical data indexing: Percentage 7.50E+01%, Size 5.00E-03m, Size 8.00E-03m

DOI: 10.6041/j.issn.1000-1298.2025.08.068

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

63. Path Planning Method for Hole Tray Seedling Transplanting Based on Improved Ant Colony Algorithm

Accession number: 20253519069954

Title of translation: 基于改进蚁群算法的穴盘苗补苗移栽路径规划方法

Authors: Ren, Ling (1, 2); Cui, Jianpu (1, 3); Zhang, Conghua (1, 2); Yang, Miao (1, 3); Zhang, Yuquan (1, 3)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832003, China; (2) Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi; 832003, China; (3) Corps Key Laboratory of Modern Agricultural Machinery, Shihezi; 832003, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 293-302 and 379

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to improve the efficiency of greenhouse tomato plug seedling transplanting, the transplanting path was planned to reduce the length and computation time of the path planning, improve the efficiency of mechanical arm transplanting, and shorten the reaction time. A path planning method for robotic arm seedling transplanting was proposed based on improved ant colony optimization (improved ACO) algorithm. Firstly, a multi factor heuristic function was adopted, in which an angle factor was added to enhance the global planning of the path. Secondly, to solve the problem of slow convergence speed in traditional ant colony algorithms, adaptive volatility coefficients and dynamic weight coefficients were introduced. Finally, in order to address the problem of complex and disordered pheromones in the context of seedling path planning, edge distance factors were added and pheromone thresholds were set under pheromone updates, with the aim of reducing path planning time and accelerating algorithm convergence. The simulation results showed that compared with traditional optimization algorithms, the improved ant colony algorithm model can effectively optimize the path of seedling transplantation. Under the experimental conditions of 128 hole tray, the path planning length of this model was shortened by 14. 65% compared with that of the fixed sequence method, 6. 76% compared with that of the ant colony algorithm model, 3. 68% compared with that of the genetic algorithm model, and 1. 01% compared with that of the clone selection algorithm model. By comparison, it can be seen that improving the ant colony algorithm model was more beneficial for planning the path of transplanting seedlings. This model can serve as the control basis for the path planning algorithm of mechanized transplanting of greenhouse plug seedlings. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Ant colony optimization

Controlled terms: Fruits? - ?Genetic algorithms? - ?Greenhouses? - ?Heuristic algorithms? - ?Mechanical efficiency? - ?Motion planning? - ?Plants (botany)? - ?Robotic arms? - ?Seed

Uncontrolled terms: Algorithm model? - ?Angle factors? - ?Ant colonies algorithm? - ?Improved ant colony algorithm? - ?Path planning method? - ?Pheromone? - ?Plug seedling? - ?Seedling path planning? - ?Seedlings transplanting? - ?Tomato plug seedling

Classification code: 101.6.1 Robotic Assistants? - ?103 Biology? - ?731.5 Robotics? - ?821.5 Agricultural Products? - ?821.7 Farm Buildings and Other Structures? - ?1101 Artificial Intelligence? - ?1106 Computer Software, Data Handling and Applications? - ?1106.1 Computer Programming? - ?1201.7 Optimization Techniques? - ?1301.1.1 Mechanics

Numerical data indexing: Percentage 1.00E00%, Percentage 6.50E+01%, Percentage 6.80E+01%, Percentage 7.60E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.027

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

64. Modification of Coastal Saline Soil by Calcium Carbonate Cementation Induced by Sporosarcina pasteurii

Accession number: 20253519059794

Title of translation: 海涂盐渍土巴氏芽孢杆菌诱导碳酸钙胶结改性试验研究

Authors: She, Dongli (1); Yang, Xiaolong (1); Bai, Yinhao (1); Han, Xiao (2); Tang, Shengqiang (1); Yang, Darning (1); Wang, Hongde (3); Sun, Xiaoqin (3)

Author affiliation: (1) College of Agricultural Science and Engineering, Hohai University, Nanjing; 210098, China; (2) Jiangsu Surveying and Design Institute of Water Resources Co., Ltd., Yangzhou; 225000, China; (3) College of Soil and Water Conservation, Hohai University, Nanjing; 210098, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 614-620

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to explore the effects of the technology of calcium carbonate precipitation induced by Sporosarcina pasteurii on the soil structure and water infiltration capacity of coastal saline soil, laboratory tests of calcium carbonate consolidation were carried out by setting different salt content and different concentrations of bacterial solution-cementing fluid as experimental treatment. Scanning electron microscopy (SEM) image results showed that the calcium carbonate crystals induced by Sporosarcina pasteurii were rhombic hexahedral samples, and X-ray diffraction (XRD) results showed that Sporosarcina pasteurii induced cementation to form calcite-type calcium carbonate under different treatments. Because the application of high concentration of bacterial and cementing solution would produce more calcium carbonate, resulting in more obvious adhesion effect of soil particles, but it would weaken the soil improvement effect. The macroaggregate content (R0 25), mean weight diameter (MWD) and geometric mean diameter (GMD) of the surface 5 cm soil was improved by Sporosarcina pasteurii, which was induced by calcium carbonate cementation were 2. 22, 1. 62 and 1. 24 times of those of the blank group, respectively, the pore density and porosity were 2. 37 and 3. 38 times of those of the blank group, respectively, and the saturated hydraulic conductivity (Kt) was 3.74 times, indicating that this technology could improve soil agglomeration and water stability, improve soil pore structure, and thus increase the water infiltration capacity of the coastal soil. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Scanning electron microscopy

Controlled terms: Bacteria? - ?Calcite? - ?Calcium? - ?Calcium carbonate? - ?Carbonation? - ?Cementing (shafts)? - ?Consolidation? - ?Hydraulic conductivity? - ?Infiltration? - ?Pore structure ? - ?Precipitation (chemical)? - ?Soil cement? - ?Soil pollution? - ?Soil testing? - ?Soils? - ?X ray diffraction

Uncontrolled terms: Calcite precipitation? - ?Carbonate cementation? - ?Coastal saline soils? - ?Hydraulic properties? - ?Infiltration capacity? - ?Microbially induced calcite precipitation? - ?Pores structure? - ?Soil pore structure? - ?Soil pores? - ?Water infiltration

Classification code: 103.1 Microbiology? - ?202.9.2 Alkaline Earth Metals? - ?214 Materials Science? - ?217.2 Concrete? - ?405.2 Construction Methods? - ?406.2 Roads and Streets? - ?482.1 Minerals? - ?483.1 Soils and Soil Mechanics? - ?802.2 Chemical Reactions? - ?802.3 Chemical Operations? - ?804 Chemical Products? - ?804.2 Inorganic Compounds? - ?1301.1.2 Physical Properties of Gases, Liquids and Solids? - ?1301.2.1 High Energy Physics? - ?1301.3.1 Microscopy? - ?1401.1 Hydraulics? - ?1502.1.1.3 Soil Pollution? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Size 5.00E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.058

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

65. Remediation of Oxytetracycline-contaminated Salinized Soil by Oxytetracycline-degrading Bacteria and Plants

Accession number: 20253519059779

Title of translation: 降解菌和植物对土霉素污染盐渍化土壤的修复

Authors: Shen, Cong (1); Wang, Yuanduo (1); Zhang, Junhua (1, 2); Liu, Jili (2)

Author affiliation: (1) School oj Life Sciences, Ningxia University, Yinchuan; 750021, China; (2) School of Ecology and Environment, Ningxia University, Yinchuan; 750021, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 621-633 and 715

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Livestock manure is an important reservoir of antibiotics and antibiotic resistance genes (ARGs), and the application of organic fertilizer is one of the effective ways to improve salinized soil. Therefore, salinized soil has the risk of being contaminated by antibiotics. Oxytetracycline is a common and high content antibiotic in livestock and poultry manure. The microbial and phytoremediation effects of salinized soil contaminated by oxytetracycline were studied by pot experiment. The results showed that compared with the original soil + oxytetracycline (CK), the contents of oxytetracycline in the soil inoculated with degrading bacteria (inoculation bacteria, IB) alone, lettuce (growing lettuce, GL) alone and combined remediation of degrading bacteria and lettuce (BLC) were decreased by 42. 47%, 39.01% and 40.44% at 30 d, respectively. The degradation rate was the highest when DB1 was inoculated alone, reaching 54.93%. Compared with CK treatment, IB, GL and BLC treatments generally significantly reduced the total relative abundance of ARGs in soil by 53. 84% ~ 73. 86%, 63. 64% and 64. 17% -76. 47% at 30 d, respectively, mainly by reducing quinolones (qepA, oqxB and qnrB, etc.) and tetracycline ARGs (tetPB — 01, tetPB —05 and tetR — 02, etc.). Three ways changed the diversity of soil bacteria. At 30 d, IB, GL and BLC decreased the relative abundance of Actinobacteriota, significantly increased the number of Woeseia and decreased the number of Luteimonas at the genus level. The three repair methods increased the risk of horizontal transfer of individual genes (such as tetX, tetG — 01 and ermF) . Overall, single inoculation of degrading bacteria, single planting of lettuce and combined remediation of degrading bacteria and lettuce can reduce the content of oxytetracycline in salinized soil, which had a significant inhibitory effect on the production of ARGs; however, the remediation ability of pure plant degradation to oxytetracycline-contaminated saline soil was limited, and the remediation effect of inoculation with highly efficient degrading bacteria was the best. Inoculation of degrading bacteria alone reduced the spread of ARGs mainly by affecting the diversity and structure of soil microbial communities and inhibiting the horizontal gene transfer of mobile genetic elements (MGEs) . Combined remediation mainly reduced the abundance of ARGs by reducing the diversity of microbial communities and changing their structure. The results aimed to provide a scientific basis for reducing the spread of ARGs in salinized soil-plant ecosystems. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 47

Main heading: Bioremediation

Controlled terms: Antibiotics? - ?Bacteria? - ?Biodegradation? - ?Cultivation? - ?Degradation? - ?Fertilizers? - ?Genes? - ?Manures? - ?Plants (botany)? - ?Repair ? - ?Soil pollution? - ?Soil pollution control? - ?Soils

Uncontrolled terms: Antibiotic resistance gene? - ?Antibiotic resistance genes? - ?Degrading bacteria? - ?High-content? - ?Livestock manure? - ?Microbial communities? - ?Microbials? - ?Organic fertilizers? - ?Poultry manure? - ?Relative abundance

Classification code: 101.7 Biotechnology? - ?101.7.1 Genetic Engineering? - ?102.2.1 Pharmaceutics and Drug Products? - ?103 Biology? - ?103.1 Microbiology? - ?483.1 Soils and Soil Mechanics? - ?801.1 Biochemistry? - ?802.2 Chemical Reactions? - ?821.3 Agricultural Chemicals? - ?821.4 Agricultural Methods? - ?821.6 Agricultural Wastes? - ?913.5 Maintenance? - ?1501.3 Sustainable Waste Managment? - ?1502.1 Environmental Impact and Protection? - ?1502.1.1.3 Soil Pollution? - ?1502.1.1.4.3 Soil Pollution Control? - ?1502.4 Biodiversity Conservation

Numerical data indexing: Percentage 1.70E+01%, Percentage 3.901E+01%, Percentage 4.044E+01%, Percentage 4.70E+01%, Percentage 5.493E+01%, Percentage 6.40E+01%, Percentage 8.40E+01%, Percentage 8.60E+01%

DOI: 10.6041/j.issn.1000-1298.2025.08.059

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

66. Identification of Key Areas for Ecological Protection and Restoration in the Chengdu-Chongqing Economic Circle Based on Ecological Security Pattern

Accession number: 20253519059777

Title of translation: 基于生态安全格局的成渝双城经济圈生态保护修复关键区域识别

Authors: Tao, Dan (1, 2); Wei, Xiang (1, 2); Hu, Chengchen (1, 2); Wang, Jiufeng (1, 2); Li, Wanyi (1, 2); Zhang, Qianxi (1)

Author affiliation: (1) Key Laboratory of Earth Exploration and Information Techniques, Ministry of Education, Chengdu University of Technology, Chengdu; 610059, China; (2) College of Geophysics, Chengdu University of Technology, Chengdu; 610059, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 544-554

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Ecological protection and restoration is crucial to the maintenance of national ecological security, and the challenge of national spatial planning is to scientifically and rationally determine the key ecological protection and restoration areas. Taking the Chengdu — Chongqing Economic Circle as the study area, and the 2020 land use type data as the basic data, the MSPA — InVEST — Conefor research model was utilized to screen the ecological source areas, and the MCR model was adopted to construct the resistance surface in order to extract the ecological corridors, which together constituted the basic ecological security pattern. The ecological source areas and ecological corridors together constituted the basic ecological security pattern. Based on the circuit theory, the ecological pinch points, ecological obstacle points and ecological break points were extracted, and finally, the key areas for ecological protection and restoration were identified by the hierarchical method and superposition analysis. The results showed that the total area of ecological source area was 27 363. 30 km, accounting for 14. 50% of the total area of the whole region; there were 62 ecological corridors, with a total length of 5 090. 92 km; the total area of ecological pinch points was 2 260. 58 km, of which there were 196 level 1 ecological pinch points; the total area of ecological obstacles was 4 555. 50 km, of which there were 124 level 1 ecological obstacles; and there were 1 154 breakpoints, including 138 railroad breakpoints, 557 highway breakpoints and 459 main road breakpoints. Based on the extracted and identified spatial elements, combined with the natural conditions and relevant policies, the ecological security pattern of the Chengdu _ Chongqing Economic Circle was constructed with “ four zones”, “four screens” and “six corridors”. Targeted ecological protection and restoration strategies were proposed according to the key areas of ecological protection and restoration to promote the self-regulation and restoration of the ecosystem, which can provide a reference basis for the benign development of the ecosystem. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Circuit theory

Controlled terms: Ecosystems? - ?Environmental protection? - ?Hierarchical systems? - ?Investments? - ?National security? - ?Railroads? - ?Restoration

Uncontrolled terms: Chengdu? - ?Chongqing? - ?Circuits theory? - ?Ecological protection? - ?Ecological security? - ?Ecological security pattern? - ?Economic circles? - ?Key area identification? - ?Security patterns? - ?The chengdu - chongqing economic circle

Classification code: 404 Civil Defense and Military Engineering? - ?703.1 Electric Networks? - ?911.2 Industrial Economics? - ?913.5 Maintenance? - ?961 Systems Science? - ?1108 Security and Privacy? - ?1502.1 Environmental Impact and Protection? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 5.00E+01%, Size 3.00E+04m, Size 5.00E+04m, Size 5.80E+04m, Size 9.20E+04m

DOI: 10.6041/j.issn.1000-1298.2025.08.052

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

67. Causal Event Extraction Driven Key Legal Element-aware Retrieval Model of Forestry Legal Case

Accession number: 20253519072465

Title of translation: 因果事件提取驱动的林业法律案件关键法律要素感知检索模型

Authors: Tian, Xuan (1, 2); Xie, Geyun (1, 2); Wu, Zhichao (1, 2)

Author affiliation: (1) School of Information Science and Technology, School of Artificial Intelligence, Beijing Forestry University, Beijing; 100083, China; (2) Engineering Research Center for Forestry-oriented Intelligent Information Processing, National Forestry and Grassland Administration, 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: 8

Issue date: August 2025

Publication year: 2025

Pages: 411-418 and 446

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Forestry legal ease retrieval aims to identify historical forestry legal judgment eases with faets similar to the input ease, which plays a central role in intelligent forestry legal systems. Existing legal ease retrieval models failed to adequately consider the key legal elements embedded in the specific structure of legal documents, thus hindering their ability to accurately use the deep semantic information contained in these key legal elements, ultimately leading to inferior performance when retrieving similar candidate eases. In forestry legal case documents, key legal elements usually appeared in various causal events with forest trees as the main body. Based on this, the causal event extraction-driven key legal element-aware model (CEKLE) was proposed, which was a forestry legal case retrieval model with awareness of key legal elements driven by causal event extraction. This model decomposed forestry legal document into five main sections; “Introduction”, “Facts”, “Analysis”, “Judgment”, and “Tail”. On this basis, it focused on the two parts of “Fact” and “Analysis”, by combining causal event extraction, the corresponding causal events can be obtained, so as to accurately perceive the position of the key legal elements of the legal case, fully excavate the key legal semantic information, and improve the accuracy of forestry legal retrieval. The experimental results obtained from two different datasets clearly demonstrated that CEKLE achieved a better performance than the most advanced baseline model in the task of forest legal ease retrieval. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Extraction

Controlled terms: Forestry? - ?Information retrieval? - ?Information retrieval systems? - ?Laws and legislation? - ?Search engines? - ?Semantics? - ?Timber

Uncontrolled terms: Case retrieval? - ?Causal event extraction? - ?Events extractions? - ?Forestry legal case retrieval? - ?Key legal element? - ?Legal case? - ?Legal documents? - ?Retrieval models? - ?Semantics Information? - ?Structure partition

Classification code: 209.2 Wood and Wood Products? - ?217.5.3 Wood Structural Materials? - ?802.3 Chemical Operations? - ?821.1 Woodlands and Forestry? - ?903.2 Information Dissemination? - ?903.3 Information Retrieval and Use? - ?971 Social Sciences? - ?1106 Computer Software, Data Handling and Applications

DOI: 10.6041/j.issn.1000-1298.2025.08.038

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

68. Turning Mechanism and Performance Testing of Crawler Chassis for Panax notoginseng Harvester

Accession number: 20253519059770

Title of translation: 履带式三七收获机底盘转弯机理与性能试验

Authors: Wang, Faan (1, 2); Feng, Yanyi (1); Wang, Di (3); Zhang, Zhaoguo (1); Yin, Guodong (2); Shen, Cheng (2, 4); Liang, Jinhao (4); Lu, Yanbo (5); Jiang, Shifei (1); Li, Zhi (6); Tan, Feiyang (1)

Author affiliation: (1) Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming; 650500, China; (2) School of Mechanical Engineering, Southeast University, Nanjing; 211100, China; (3) Heilongjiang Vocational College for Nationalities, Harbin; 150066, China; (4) Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing; 210014, China; (5) School of Vehicle and Mobility, Tsinghua University, Beijing; 100062, China; (6) Yunnan Tobacco Machinery Co., Ltd., Kunming; 650100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 692-703

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: For the existing crawler type Panax notoginseng harvester, when operating on hilly-mountainous terrain its turning mechanism is unclear and running stability is poor. The turning mechanics and infield steering performance of the crawler chassis in a hilly-mountainous, heavy clay soil environment were investigated. In view of the actual Panax notoginseng cultivation conditions in such regions, chassis performance metrics were analyzed, including turning radius, sinkage, soil resistance, and slip ratio. Firstly, the basic principles of crawler chassis turning were presented, and three steering modes, the same direction differential steering, single side braking steering, and counter direction (reverse) differential steering, were analyzed theoretically. Nextly, theoretical calculations were carried out for chassis sinkage, slip ratio, spin to slip ratio, tractive resistance, and turning radius. Concurrently, RecurDyn simulations were conducted under the three steering conditions to evaluate turning radius, sinkage, and soil resistance. Simulation results indicated that the forces on the crawler chassis during turning were significantly greater than those during straight line travel; the total force on the track plates were decreased as forward speed was increased, while sinkage remained essentially unchanged. Finally, field trials measured the actual turning radius, slip ratio, and sinkage of the crawler chassis. Test results showed that the minimum turning radius was 1 010 mm on a hard paved surface, 1 150 mm on compacted soil, and 1 360 mm on loose soil; these experimental values closely matched the simulation predictions. This research can lay a foundation for the structural and parameter optimization of crawler chassis transport machinery. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Chassis

Controlled terms: Clay? - ?Cultivation? - ?Elasticity? - ?Harvesters? - ?Soil testing? - ?Turning

Uncontrolled terms: Crawler chassi? - ?Crawler types? - ?Differential steering? - ?Field steering test? - ?Panax notoginseng? - ?Panax notoginseng harvester? - ?Performance testing? - ?Slip ratio? - ?Turning mechanism? - ?Turning radius

Classification code: 214.1.3 Elasticity, Plasticity, Creep and Deformation? - ?217.4.1 Brick and Mortar? - ?483.1 Soils and Soil Mechanics? - ?604.2 Machining Operations? - ?662.3 Automobile Components and Materials? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Size 1.00E-02m, Size 1.50E-01m, Size 3.60E-01m

DOI: 10.6041/j.issn.1000-1298.2025.08.065

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.

69. Design and Experimentation of Underwater Observation Robot for Estimating Sea Cucumber Distribution Density

Accession number: 20253519059767

Title of translation: 面向海参分布密度估计的水下观测机器人设计与实验

Authors: Wang, Wenliang (1, 2); Liu, Xiaoyang (1, 2); Yin, Changkun (1, 2); Zhao, Heming (1, 2); Chen, Qijun (3); Zhang, Jiaxu (2); Zhang, Haiguang (2); Li, Guodong (4); Wang, Wei (1, 5); Lin, Yuanshan (1, 5)

Author affiliation: (1) School oj Information Engineering, Dalian Ocean University, Dalian; 116023, China; (2) Dalian Key Laboratory oj Smart Fisheries, Dalian; 116023, China; (3) Dalian Xinyulong Marine Biological Seed Industry Science and Technology Co., Ltd., Dalian; 116007, China; (4) Institute oj Fisheries Machinery and Instrumentation, Chinese Academy of Fishery Sciences, Shanghai; 200092, China; (5) Key Laboratory of Facilities Fisheries, Ministry of Education, Dalian Ocean University, Dalian; 116023, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 8

Issue date: August 2025

Publication year: 2025

Pages: 535-543 and 601

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to overcome the inefficiency, randomness, and insufficient coverage of traditional manual inventory methods in sea cucumber aquaculture, an autonomous underwater observation robot (ROV) with real-time area calculation capabilities was developed. The system was designed to enable comprehensive scanning of breeding zones and accurate biomass estimation. Firstly, an integrated ROV framework was proposed, combining visual acquisition, motion control, and dynamic area computation. Secondly, based on the operational requirements of sea cucumber ponds, autonomous motion functions, including depth-holding and course-keeping were implemented by using PID control algorithms. The ROV pose was determined through kinematic modeling, and a “ crisscross” path planning strategy was employed to ensure complete seabed coverage. Real-time scanning area estimation was performed using calibrated camera parameters to enhance measurement accuracy. Experimental validation in simulated and real aquaculture environments demonstrated that the ROV achieved a vertical positioning accuracy of + 0.02 m, an area calculation error below 2.31%, and a density estimation error under 5%. Furthermore, the system exhibited robust performance under varying turbidity conditions, maintaining stable vision-based detection even in low-visibility scenarios. The closed-loop control architecture, integrating perception, navigation, and computation, effectively addressed the challenges of autonomous underwater operation and real-time monitoring. Compared with conventional methods, the proposed system significantly improved inventory efficiency while reducing labor costs and human error. Future work would focus on multi-ROV collaborative operation and machine learning-enhanced sea cucumber recognition for large-scale aquaculture applications. This research contributed a fully automated, high-precision monitoring solution for sustainable aquaculture management. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Wages

Controlled terms: Aquaculture? - ?Autonomous underwater vehicles? - ?Cameras? - ?Closed loop control systems? - ?Errors? - ?Motion planning? - ?Remotely operated vehicles? - ?Underwater structures? - ?Visual servoing

Uncontrolled terms: Area calculation? - ?Calculation capability? - ?Camera calibration? - ?Density estimation? - ?Distribution density? - ?Real- time? - ?Regional coverage? - ?Sea cucumber? - ?Underwater observation? - ?Underwater observation robot

Classification code: 408 Structural Design? - ?472 Ocean Engineering? - ?674.1 Small Marine Craft? - ?731.1 Control Systems? - ?731.1.1 Error Handling? - ?731.5 Robotics? - ?732 Control Devices? - ?742.2 Photographic and Video Equipment? - ?821.4 Agricultural Methods? - ?912.3 Personnel? - ?961 Systems Science? - ?1101 Artificial Intelligence? - ?1107 Human-Machine Systems

Numerical data indexing: Percentage 2.31E+00%, Percentage 5.00E+00%, Size 2.00E-02m

DOI: 10.6041/j.issn.1000-1298.2025.08.051

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

Compilation and indexing terms, Copyright 2025 Elsevier Inc.