2025年第5期

2025年第5期

2025年第5期共收录59篇

1. Design and Experiment of Variable-diameter Stubble Cutting Disc Device for No-till Planter

Accession number: 20252118471998

Title of translation: 免耕播种机可变径切茬圆盘装置设计与试验

Authors: Zhong, Guangyuan (1, 2); Li, Hongwen (1, 2); Lu, Caiyun (1); Wang, Chao (1); Tong, Zhenwei (1); Bi, Jinshuo (1)

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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 235-245

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A stubble cutter was designed with manually variable disc diameter adjustment for the problem that the passive disc stubble cutter on the no-till planter in the no-tillage seeder in the Huang — Huai -Hai wheat and com area has a low straw cutting rate in the case of large amount of straw cover or insufficient soil strength. The variable-diameter stubble cutting blade was equipped with a disc diameter adjustment mechanism, which can adjust the disc diameter according to the soil characteristics of different fields and the amount of straw stubble cover on the ground surface in order to realize efficient straw cutting. Combined with theoretical analysis, it was determined that the angle of two edges of star teeth of blade was 110°, the minimum radius of variable diameter disc stubble cutter was 230 mm, the maximum radius was 280 mm, and when the stubble cutter was adjusted to a certain diameter, the variable diameter mechanism had a self-locking function to ensure that the diameter of blade was fixed. The results of soil bin test showed that the diameter of 460 mm variable diameter stubble cutting disc, flat disc, notched disc, and corrugated disc, under the same working conditions, the disc into the soil depth was deeper than the shallow cutting Performance, into the soil depth of 10 cm, variable diameter stubble cutting disc straw cutting performed better than other types of discs, at this time, the straw cutting rate was 75. 16%, the traction force was 355. 27 N. Soil bin Performance tests showed that the larger the diameter of the variable diameter stubble cutting dise was, the better the straw cutting Performance was. In the diso diameter of 560 mm, the straw-cutting rate was 93. 25%. Field Validation tests showed that the variable-diameter stubble-cutting diso device in the disc diameter of 560 mm, the straw cutting rate was 98. 33%, which was good to meet the no-tillage sowing Operation of the Huang — Huai — Hai regions of wheat agronomic and technical requirements. The research result can provide a reference for the design of the stubble-cutting device for no-tillage implements. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Agricultural implements

Controlled terms: Disks (machine components) - Harvesters

Uncontrolled terms: Adjustment mechanisms - Cutting discs - Cutting rate - Diameter adjustment mechanism - Disk diameters - No-till planters - No-tillage seeders - Star-toothed blade - Variable diameter - Variable diameter disk

Classification code: 601.2 Machine Components - 821.2 Agricultural Machinery and Equipment

Numerical data indexing: Force 2.70E 01N, Percentage 1.60E 01%, Percentage 2.50E 01%, Percentage 3.30E 01%, Size 1.00E-01m, Size 2.30E-01m, Size 2.80E-01m, Size 4.60E-01m, Size 5.60E-01m

DOI: 10.6041/j.issn.1000-1298.2025.05.022

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

2. Multi-objective Otimization of Hydraulic Performance of Low Specific Speed Stamp Pump Impeller Based on PSO Algorithm

Accession number: 20252118471993

Title of translation: 基于PSO算法的低比转数冲压离心泵水力性能多目标优化

Authors: Zheng, Shuihua (1); Zhao, Xueyan (1); Zhang, Cheng (1); Li, Yiliang (1); Chai, Min (1)

Author affiliation: (1) College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou; 310014, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 353-360

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issue of low hydraulic Performance in low-specific-speed stamping eentrifugal pumps, focusing on the CDL1 multi-stage stamping centrifugal pump, using its first-stage impeller as the research object, by eombining numerical Simulation and experimental testing methods, a comprehensive analysis of the hydraulic Performance of the first-stage impeller was conducted. Given that the hydraulic Performance of low-specific-speed impellers was influenced by multiple factors, Latin hypercube sampling (LHS) was employed to sample various design variables of the first-stage impeller, forming a sample space and obtaining the corresponding Performance parameters. A Kriging Surrogate model was then established to analyze the sensitivity of each parameter to the hydraulic Performance of the impeller. The critical influence parameters of the impeller were selected as the input for the particle swarm optimization algorithm (PSO), and multi-parameter optimization design was carried out. On this basis, the hydraulic Performance and internal flow mechanism of the impeller were investigated in depth. The results showed that the hydraulic Performance of the optimized impeller was significantly improved compared with the original design, with the efficiency at the rated point increased by 2. 8 percentage points and the single-stage head increased by 0. 4 m. Additionally, the optimization process revealed that the impeller’s blade angle, inlet and outlet diameters, and blade thickness were the most sensitive parameters affecting hydraulic Performance. The improved design not only significantly enhanced the Overall efficiency and head but also optimized the flow distribution, reduced turbulence, minimized energy losses, improved fluid dynamics, and increased operational stability, leading to better Performance, reliability, and long-term durability in practical applications. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Convergence of numerical methods

Controlled terms: Centrifugal pumps - Stamping - Viscous flow - Vortex flow

Uncontrolled terms: Experimental testing - Hydraulic performance - Low specific speed stamp pump - Low specific-speed - Multi objective - Optimisations - Optimization of hydraulic performance - Particle swarm optimization algorithm - Pump impeller - Stampings

Classification code: 201.5.2 Metal Forming - 301.1 Fluid Flow - 301.1.5 Flow of Fluid-Like Materials - 609.2 Pumps - 1201.9 Numerical Methods

Numerical data indexing: Size 4.00E 00m

DOI: 10.6041/j.issn.1000-1298.2025.05.033

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

3. Study on the Correlation Between Stem Water Storage Capacity and Sap Flow in Standing Trees

Accession number: 20252118477593

Title of translation: 活立木茎干蓄水能力与液流相关性研究

Authors: Zhao, Yandong (1, 2); Liu, Xiaofeng (1); Nie, Liyang (3); Li, Jilong (3); Zhu, Lin (3)

Author affiliation: (1) School of Technology, Beijing Forestry University, Beijing; 100083, China; (2) Beijing Laboratory of Urban and Rural Eeologieal Environment, Beijing Forestry University, Beijing; 100083, China; (3) Beijing Jindu Garden Landscaping Co., Ltd., Beijing; 100140, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 492-500

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The dynamic relationship between stem water content and sap flow is of great significance for understanding the mechanisms of plant water transport and transpiration regulation. However, traditional plant water monitoring methods are limited by spatial and temporal resolution, making it difficult to capture subtle changes in plant water dynamics. An intelligent monitoring System based on the i. MX6ULL ehip was developed. By integrating advanced sensor technology, data acquisition, and analysis methods, real-time monitoring of key parameters such as stem water content, sap flow rate, transpiration, soil moisture, and air temperature and humidity was achieved. Long-term field monitoring of Ginkgo biloba trees verified the system’s stability and reliability. Statistical analysis results showed that the sap flow and stem water content of Ginkgo biloba exhibited significant trends at different growth stages, with sap flow rates ranging from 0. 82 cm/h to 20. 52 cm/h. During the growing season, the stem water content derivative showed a significant negative correlation with sap flow data (Pearson correlation coefficient was greater than - 0. 7). As sap flow was increased during the growing season, stem water content was decreased, and the rate of stem water change could reflect the trend of sap flow to a certain extent. Additionally, for every 1 °C increase in air temperature, sap flow rate was increased by an average of 8. 6%, while for every 10 percentage points increase in air relative humidity, the sap flow rate was decreased by 27. 3%. The research result can provide experimental evidence for the relationship between water transport in Standing trees and offer scientific support for plant physiology research and eeologieal management. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Water content

Uncontrolled terms: Intelligent monitoring systems - Plant water - Real time monitoring - Sap flow - Sap flow rate - Sensor technologies - Standing tree - Stem water content - Stem water storage capacity - Water storage capacity

Classification code: 941.6 Moisture Measurements

Numerical data indexing: Percentage 3.00E 00%, Percentage 6.00E 00%, Size 5.20E-01m, Size 8.20E-01m, Temperature 2.74E 02K

DOI: 10.6041/j.issn.1000-1298.2025.05.047

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

4. Optimization Design and Experiment of Folded-wing Subsoiler for Compacted Leymus chinensis Grassland

Accession number: 20252118471986

Title of translation: 板结羊草地折翼式松土铲优化设计与试验

Authors: Zhang, Xuening (1); You, Yong (1); Wang, Zhaoyu (1); Zhang, Yuzhuo (1); Liao, Yangyang (1); Wang, Dewei (1, 2); Wang, Decheng (1)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Sehool of Mechanical Electrification Engineering, Tarim University, Alar; 843300, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 213-221

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: There is a lack of soil loosening components suitable for grassland ploughing. To better break the compacted structure of grassland soil, a special soil loosening component for grassland was designed; a folded-wing subsoiler. Optimization of the combination of structural parameters was carried out by using the shank slip angle, wing width and tip entry angle of a folded-wing subsoiler as test factors, and tillage resistance, furrow contour area and ridge contour area as target parameters. The working Performance (ridge contour area, pit contour area, soil disturbance coefficient, soil bulkiness, turning rate, and surface flatness) of the folded-wing subsoiler and three traditional subsoilers (chisel point, diamond point, and arrow point) in grassland soil was compared. The test results showed that the optimal combination of the structural parameters of the folded-wing subsoiler was as follows; the wing width was 40 mm, the shank slip angle was 20°, and the tip entry angle was 20°. At this point, the optimal Solution for the objective values was 6 140 N of tillage resistance, 160 cm of pit contour area, 68 cm of ridge contour area. Compared with the three traditional plough points, the surface of the grassland after the Operation of the folded-wing subsoiler was more flat, and there were fewer turning pieces, and all of them were small pieces of soil; at the same operating speed, the pit contour area and soil disturbance coefficient caused by the folded-wing subsoiler were the largest, while the ridge contour area, soil bulkiness, turning rate and surface flatness were the smallest. Therefore, the grassland loosening Performance of the folded-wing subsoiler was better than that of the traditional chisel point, diamond point, and arrow point. The research results can provide technical reference for the creation of key tillage components suitable for mechanized improvement of compacted grassland. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 21

Main heading: Agricultural implements

Controlled terms: Harvesters - Milking machines

Uncontrolled terms: Compacted grassland - Folded-wing subsoiler - Grassland improvement - Grassland soils - Slip angle - Soil disturbances - Soil disturbanee - Soil loosening - Structural parameter - Tillage resistance

Classification code: 821.2 Agricultural Machinery and Equipment

Numerical data indexing: Force 1.40E 02N, Size 1.60E 00m, Size 4.00E-02m, Size 6.80E-01m

DOI: 10.6041/j.issn.1000-1298.2025.05.020

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

5. Research Progress on Digital Cotton Technology and Equipment

Accession number: 20252118472030

Title of translation: 数字棉花技术与装备研究进展

Authors: Zhang, Ruoyu (1, 2); Zhang, Jianqiang (1, 2); Xia, Bin (1, 3); Wu, Chao (1, 3); Chang, Jinqiang (1, 2); Wang, Sanhui (1, 3)

Author affiliation: (1) College of Mechanieal and Electrieal Engineering, Shihezi University, Shihezi; 832003, China; (2) Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi; 832003, China; (3) Technology Innovation Center of Smart Farm Digital Equipment, Xinjiang Production and Construction Corps, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 1-16

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: As an important Strategie material in China, the digital upgrading of eotton industry is of great significance to the realization of agrieultural modernization and sustainable development of border eeonomy. The research progress of digital cotton technology System was systematically reviewed, and the conceptual framework of “digital cotton” was proposed, covering the whole industrial chain links such as planting management, yield measurement and harvesting, acquisition and processing, fair inspection, warehousing and logistics, and whole-process quality traceability. Focusing on the analysis of the application Status and key technological breakthroughs of digital equipment in precision seeding, intelligent water and fertilizer control, unmanned aerial vehicle plant protection, machine cotton picking monitoring and other links through the Integration of Internet of Things, remote sensing, big data and artificial intelligence technology, the problems of insufficient equipment adaptability, data islands and cost-benefit imbalance in the current technology application were revealed. It can provide theoretical support for the quality and efficiency improvement and high-quality development of the cotton industry. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 89

Main heading: Modernization

Controlled terms: Agricultural robots - Chains - Costs

Uncontrolled terms: Chain digitisation - Conceptual frameworks - Digital technologies - Full industry chain digitization - Industrial chain - Industry chain - Intelligent equipment - Quality traceabilitys - Technology and equipments - Technology system

Classification code: 601.2 Machine Components - 602.1 Mechanical Drives - 731.6 Robot Applications - 821.2 Agricultural Machinery and Equipment - 901 Engineering Profession - 911 Cost and Value Engineering; Industrial Economics

DOI: 10.6041/j.issn.1000-1298.2025.05.001

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

6. Moisture Regain Detection of Cotton Bündle Fibers Based on Resistance Method

Accession number: 20252118472002

Title of translation: 基于电阻法的棉花束纤维回潮率检测方法

Authors: Zhang, Jianqiang (1, 2); Huang, Jie (1, 3); Chang, Jinqiang (1, 2); Cui, Guojin (1, 2); Wang, Yang (1, 2); Zhang, Ruoyu (1, 2)

Author affiliation: (1) College of Mechanieal and Electrieal Engineering, Shihezi University, Shihezi; 832003, China; (2) Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi; 832003, China; (3) Innovation Center for Digital Equipment and Technology for Smart Earms, Xinjiang Production and Construction Corps, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 150-158

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The moisture regain rate significantly affects the test results of cotton quality indicators. Accurately measuring the moisture regain rate is of great significance for cotton grading. Aiming at the compensation and correction of the moisture regain rate during the detection of the breaking tenacity of cotton bündle fibers, a method for detecting the moisture regain rate based on the resistance method was proposed. By building a resistance-image synchronous acquisition platform and using image features to represent the fiber thickness, the influence laws of the electrode distance, temperature, and fiber thickness on the resistance measurement during the moisture regain rate measurement were explored, and a multiple prediction model with resistance and temperature as input variables was established. Experiments showed that the gray-scale features of the image were highly correlated with the resistance value and showed a non-linear relationship, and the influence law of the fiber thickness on the resistance measurement was ascertained. The resistance value had a significant positive correlation with the electrode distance within the ränge of 2 ~ 12 mm. The intrinsic mechanism of the increase in electrode spacing leading to the expansion of resistance measurement error was explained. Based on this, an electrode distance of 2 mm was determined as the optimal detection parameter, and it was verified that there was no significant difference in the resistance of different quality fibers under this parameter (P > 0.05). Through experiments on 32 groups of cotton samples with a moisture regain rate of 4. 44% ~12. 2%, the results showed that the random forest (RF) model had the best predietion accuracy, with R~ of 0. 99 and RMSE of 0. 24%. This study broke through the limitations of traditional moisture regain detection methods for loose eotton fibers and enabled rapid measurement of bundled fibers. It can provide reliable technical support for the precise compensation and correction of physieal property indicators such as the breaking tenacity of eotton, and promote the development of intelligent eotton quality detection. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Image correlation

Controlled terms: Cotton fibers - Motion analysis - Photointerpretation - Regain - Textile fibers

Uncontrolled terms: %moisture - Breaking tenacities - Bundle fibers - Cotton bundle fiber - Electrode distances - Image analyze - Image-analysis - Moisture regain detection - Resistance measurement - Resistance method

Classification code: 213 Synthetic and Natural Fibers; Textile Technology - 213.1 Natural Fibers - 213.3 Fiber Chemistry and Processing - 742.1 Photography - 1106.3.1 Image Processing - 1106.8 Computer Vision

Numerical data indexing: Percentage 2.00E 00%, Percentage 2.40E 01%, Percentage 4.40E 01%, Size 2.00E-03m to 1.20E-02m, Size 2.00E-03m

DOI: 10.6041/j.issn.1000-1298.2025.05.015

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

7. Dynamic Evolution Characteristics of Saline-alkali Soil Shrinkage Cracks and Analysis of Non-uniform Distribution of Salt

Accession number: 20252118477627

Title of translation: 盐碱土干缩裂隙动态演化特征与盐分非均匀分布规律分析

Authors: Zhai, Yaming (1, 2); Hu, Shuxuan (1); Feng, Genxiang (1, 2); Wang, Ce (1, 2); Huang, Mingyi (1, 2); Zhao, Tao (1); Wang, Haoxuan (1)

Author affiliation: (1) College of Agricultural Scienee and Engineering, Hohai University, Nanjing; 211100, China; (2) Jiangsu Provinee Engineering Research Center for Agricultural Soil— Water Efficient Utilization, Carbon Sequestration and Emission Reduction, Nanjing; 211100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 552-559

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The surface of saline-alkali soil is often aecompanied by a complex network of shrinkage cracks. Investigating the characteristics of shrinkage cracks in saline-alkali soil and the distribution of soil salinity during the dynamic evolution process is of significant importance for scientifically formulating leaching regimes to mitigate soil salinity. Indoor soil grids were employed to investigate the dynamic evolution characteristics of shrinkage cracks in saline-alkali soil and conduct sah leaching experiments. Three initial soil salinity levels were set at 2 g/kg (Sl), 5 g/kg (S2), and 8 g/kg(S3). Digital image processing technology and morphological algorithms were employed to obtain geometric parameters and Connectivity indices of the soil cracks. The evolution process of shrinkage cracks during the drying — wetting cycles in soils with different initial salinity levels was analyzed, and simultaneous investigations were conducted into the dynamic variations of soil salinity during crack evolution. The results indicated that during the process of soil shrinkage and Cracking (soil dehumidification), an increase in initial soil salinity corresponded to increases in the crack area ratio, mean width, length density, and Connectivity index. Moreover, within a single wet-dry cycle, the crack area ratio and mean width form an “ oo “ ring shape. Concurrently, soil salinity gradually migrated toward the vicinity of the cracks, ultimately leading to a non-uniform distribution pattern with higher salinity at the edges of the crack network and lower salinity within the grid. During soil shrinkage and Cracking, the coefficient of Variation of soil salinity content within treatments Sl, S2, and S3 was increased as soil moisture was decreased, reaching 0. 235, 0. 247 and 0. 251, respectively, after crack development stabilized (at soil water content of approximately 5%). In the process of soil salinity leaching (soil hygroscopic), the crack area ratio in treatment S3 was increased by 8. 565 percentage points and 4. 208 percentage points compared with that of treatments S2 and Sl, respectively, with corresponding increase in soil desalination rates of 20.4% and 67.3%. Overall, higher initial soil salinity resulted in a greater soil leaching desalination rate. The final soil desalination rates for treatments S3, S2, and Sl were 54. 2%, 45. 0%, and 32. 4%, respectively (P © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Soil moisture

Controlled terms: Optical flows - Photointerpretation - Shrinkage - Silt - Water content

Uncontrolled terms: Area ratios - Dynamic evolution - Evolution characteristics - Initial soils - Saline-alkali soils - Salt distribution - Shrinkage cracks - Soil crack - Soil salinity - Soil shrinkage

Classification code: 214 Materials Science - 483.1 Soils and Soil Mechanics - 741.1 Light/Optics - 742.1 Photography - 941.6 Moisture Measurements

Numerical data indexing: Mass 2.00E-03kg, Mass 5.00E-03kg, Mass 8.00E-03kg, Percentage 0.00E00%, Percentage 2.00E 00%, Percentage 2.04E 01%, Percentage 4.00E 00%, Percentage 5.00E 00%, Percentage 6.73E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.053

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

8. Design and Test of Seed Reducer for Air-fed Rice - Wheat High-speed Seeder Based on CFD - DEM

Accession number: 20252118472035

Title of translation: 基于CFD-DEM的气送式稻麦兼用型高速播种机种子减速器设计与试验

Authors: Zang, Ying (1, 2); Zhang, Meilin (1, 3); Huang, Zishun (1, 3); Jiang, Youcong (1, 3); Qian, Cheng (1, 3); Wang, Zaiman (1, 4)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) State Key Laboratory of Agricultural Equipment Technology, Beijing; 100083, China; (3) Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou; 510642, China; (4) Huangpu Innovation Research Institute, South China Agricultural University, Guangzhou; 5 10715, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 222-234

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 low seed feeding accuracy and unstable seed feeding caused by the fast seed conveying speed of the air-fed rice — wheat dual-purpose high-speed seeder, a seed reducer based on the principle of cyclone deceleration was designed. The CFD —DEM eoupling Simulation method was used to carry out a single factor test to determine the main structural factors and select the appropriate size ränge. In order to determine the structural parameters of the seed reducer, the Box — Bhnken orthogonal combination Simulation test was carried out based on the single factor test results, and the results showed that the optimal structural dimensions were 82. 352 mm of cylinder diameter D, cylinder length Ht of 101.364 mm, exhaust port diameter Dp of 25.000 2 mm, cone length H, of 67. 902 5 mm, and the indica seed outlet flow velocity Vt and seed vertical velocity V2 were 5. 212 m/s and 0. 462 m/s, respectively. The seed flow velocity Vt and seed vertical velocity V2 of japonica rice were 5. 339 m/s and 0. 473 m/s, respectively. The flow velocity V, and seed vertical velocity V2 of wheat seeds were 5. 341 m/s and 0.408 m/s, respectively. The results of bench verification test showed that the vertical velocity of indica rice seeds was 0.411 m/s, japonica rice seeds was 0.452 m/s, and wheat seeds are 0. 457 m/s at the seed outlet, which was consistent with the Simulation test results. It can be seen from the bench test of strip sowing Performance that the effect of Strip arrangement with seed reducer was significantly better than that without seed reducer, and the coefficient of Variation of displacement uniformity of indica, japonica rice and wheat was decreased from 41. 61%, 25% and 37. 84% without seed reducer to 9. 10%, 8.42% and 8.49%, respectively, which met the Performance requirements of seed reducer. The results can provide guidance for improving the seeding Performance of air-fed rice-wheat seeder in the future. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Structural analysis

Uncontrolled terms: Air feeds - CFD-DEM - DEM - High Speed - High-speed seeder - Japonica rice - Rice - Seed reducer - Vertical velocity - Wheat combined use

Classification code: 408 Structural Design

Numerical data indexing: Percentage 2.50E 01%, Velocity 3.41E 02m/s, Percentage 6.10E 01%, Percentage 8.40E 01%, Percentage 8.42E 00%, Percentage 8.49E 00%, Size 1.01364E-01m, Size 2.00E-03m, Size 3.52E-01m, Size 5.00E-03m, Velocity 2.12E 02m/s, Velocity 3.39E 02m/s, Percentage 1.00E 01%, Velocity 4.08E-01m/s, Velocity 4.11E-01m/s, Velocity 4.52E-01m/s, Velocity 4.57E 02m/s, Velocity 4.62E 02m/s, Velocity 4.73E 02m/s

DOI: 10.6041/j.issn.1000-1298.2025.05.021

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

9. Real Time Visual Detection for Cluttered Targets Based on Deep Learning Acceleration Model

Accession number: 20252118477960

Title of translation: 基于深度学习加速模型的杂乱目标实时视觉检测方法

Authors: Yu, Yongwei (1); Chen, Tianhao (1); Du, Liuqing (1); Fang, Rong (1)

Author affiliation: (1) College of Mechanieal Engineering, Chongqing University of Technology, Chongqing; 400054, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 617-624

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the automatic assembly line of agricultural machinery, the on-chip resources of its embedded control platform are extremely limited, and the parameter amount of the convolutional neural network-based deep learning detection System is too large, which is difficult to be directly transplanted to the embedded platform. Therefore, a deep learning real-time detection method based on improved ResNetl8 — SSD (single shot multi-box detector) and field programmable gate array (FPGA) acceleration engine was proposed. In order to improve the accuracy of the detection model while reducing the number of parameters, a deep learning fast detection model based on ResNetl8 — SSD was proposed, which utilized the optimized and improved ResNetl8 network to replace the VGG16 predecessor network of the SSD model, and introduced a multi-branch isomorphic structure and an asymmetric parallel residual structure, so as to adapt to the complex scenes such as occlusion, dim light; and in the case of meeting the detection accuracy requirements, a dynamic fixed-variance network was used to meet the detection accuracy requirements. Under the condition of meeting the requirements of detection accuracy, the dynamic fixed-point quantization was adopted to reduce the model data volume to improve the execution efficiency of the detection model. Aiming at improving the convolutional layer in the ResNetl8 — SSD model, which consumed serious resources, an FPGA acceleration engine based on the Winograd algorithm was proposed to improve the real-time Performance of the model detection, and through the software-hardware co-design, Joint optimization was carried out from the perspectives of the hardware gas pedal and the lightweighting of the Software network, so as to achieve a balance between the lightweighting, aceeleration Performance, and accuracy in the complex scene. Experimental results on the Xilinx FPGA embedded platform showed that the detection accuracy of the proposed method reached 93. 5%, and the detection time of a single image under the operating frequency of 100 MHz was 80. 232 ms, which met the real-time demand. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Integrated circuit design

Uncontrolled terms: Detection accuracy - Detection models - Dynamic fixed-point quantization - Field programmable gate array - Field programmables - Fixed points - Objects detection - Programmable gate array - Quantisation - Winograd’s algorithms

Classification code: 714.2 Semiconductor Devices and Integrated Circuits - 904 Design

Numerical data indexing: Frequency 1.00E 08Hz, Percentage 5.00E 00%, Time 2.32E-01s

DOI: 10.6041/j.issn.1000-1298.2025.05.060

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

10. Design and Test of Operation Quality Monitoring System for Cotton Precision Film-laying Hole Seeder

Accession number: 20252118471991

Title of translation: 棉花精量铺膜穴播机作业质量监测系统设计与试验

Authors: Bai, Shenghe (1, 2); Yuan, Yanwei (1, 2); Niu, Kang (2, 3); Zhou, Liming (2, 3); Zhao, Bo (2, 3); Yu, Yongliang (4)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing; 100083, China; (3) State Key Laboratory of Agricultural Equipment Technology, Beijing; 100083, China; (4) Xinjiang Tiancheng Agricultural Machinery Manufacturing Co., Ltd., Tiemenguan; 841007, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 49-58

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to monitor the process of cotton precision seeding and laying film Operation for real-time, and improve the intelligence level of the cotton precision film-laying hole seeder, employing white light source color code sensors and high-definition network cameras as primary monitoring components, a cotton precision film-laying hole seeder Operation quality monitoring System was designed based on Vision Assistant. The System consisted of three modules: the seeding monitoring module, film-laying monitoring module and visualization module. Among them, the seeding monitoring module included a seeding Status monitoring sensor, a proximity switch sensor, etc. the film-laying monitoring module was mainly composed of a high-definition network camera; the visualization module includes data acquisition module, industrial control Computer, etc. Using Labview Software graphical programming, equipped with a multi-functional industrial Computer, this System achieved real-time monitoring of seeding quality (seeding amount, missed seeding amount), film-laying quality (lighting surface width, lighting surface soil covering width, lighting surface damage area), and Operation conditions by running specific function programs through function selection controls. The results of the quality monitoring System bench and field test results showed that the System worked stably and reliably, the monitoring accuracy rate of seeding amount reached over 92%, the monitoring accuracy rate of lighting surface width reached over 94%, the monitoring accuracy rate of lighting surface damage area reached over 81%, and the monitoring accuracy of lighting surface soil covering width reached over 90%. It met the actual requirements of the cotton precision film-laying hole seeder Operation quality monitoring System and provided technical support for quality evaluation film-laying sowing Operation. It was of great significance to improve the quality and efficiency of cotton cultivation. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Computer control

Controlled terms: Damage detection - Luminescent devices - Multitasking - Problem oriented languages - Visual languages

Uncontrolled terms: Cotton precision film-laying hole seeder - Film-laying quality monitoring - Monitoring accuracy - Operations quality - Quality monitoring - Quality monitoring system - Soil coverings - Sowing quality monitoring - Surface soil

Classification code: 741.3 Optical Devices and Systems - 913.3.1 Inspection - 1103.4 Digital Computers and Systems - 1106.1.1 Computer Programming Languages - 1106.5 Computer Applications

Numerical data indexing: Percentage 8.10E 01%, Percentage 9.00E 01%, Percentage 9.20E 01%, Percentage 9.40E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.005

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

11. Design and Experiment of Oblique Automatic Seedling Picking and Throwing Device for Vegetable Dense Transplanting

Accession number: 20252118476508

Title of translation: 蔬菜密植移栽斜置式自动取投苗装置设计与试验

Authors: Bai, Xiaohu (1, 2); Du, Guojing (1); Zhang, Zihao (1); Qiu, Shuo (1); Zhao, Bo (1); Tian, Subo (1, 2)

Author affiliation: (1) College of Engineering, Shenyang Agrieultural Vniversity, Shenyang; 110866, China; (2) Key Laboratory of Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Shenyang; 110866, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 300-308

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: According to agronomic requirements, most leafy vegetables adopt a dense transplanting mode with a plant spacing and row spacing of less than 200 mm. However, the automatic seedling picking and throwing devices developed domestically are mostly suitable for transplanting with large plant spacing and row spacing. The gantry arm type seedling picking and throwing de vice can replace manual Operations, but due to the long movement path of the seedling picking claws, the speed of seedling picking and throwing is slow. Therefore, this structure cannot meet the requirements of dense transplanting of vegetables. For the A5 — 1200 semi-automatic high-density transplanter, an oblique automatic seedling picking and throwing device was designed to address the issue of low transplanting efficiency. The device consisted of a seedling tray displacing mechanism, a picking and throwing arm, and eight claws. The seedling tray and the arm were both arranged at a 45° angle to the horizontal direction, and the claws moved back and forth in a straight line between the seedling picking position and the feeding position, shortening the seedling picking stroke. The installation positions of the claws on the picking and throwing arm were fixed, corresponding to the distance between the seedlings and the distance between the seedling feeding cup, omitting the seedling Separation step. The orderly and smoothly Operation process was ensured by the PLC control System. The ball screw module was driven by the closed-loop stepper motor in the transmission parts for both the picking and throwing arm and the seedling tray displacing mechanism. To realize the whole row interval seedling picking, the tray displacing mechanism completed a single action within 1 s, moved the seedling tray in the horizontal and longitudinal directions, and located the position of the seedlings precisely. The influences of the return speed of picking and throwing arm, the insertion depth and the penetration angle of claw on the picking and throwing effect were analyzed. The Box — Behnken response surface experiments were designed to optimize the working Parameters. The results showed that when the return speed of the picking and throwing arm was 300 mm/s, the insertion depth of claw was 31 mm, and the penetration angle of claw was 10°, the actual success rate of picking and throwing was 97.0%. Equipped with the oblique automatic taking and throwing device, the A5 — 1200 transplanter had a theoretical transplanting capacity of 7 200 plants/h, which met the technical requirements of dense transplanting. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Stepping motors

Controlled terms: Agricultural machinery

Uncontrolled terms: Dense transplanting - Insertion depth - Leafy vegetables - Oblique type - Picking and throwing device - Plant spacing - Return speed - Row spacing - Seedling tray displacing meohanism - Transplanter

Classification code: 705.3 Electric Motors - 821.2 Agricultural Machinery and Equipment

Numerical data indexing: Percentage 9.70E 01%, Size 2.00E-01m, Size 3.10E-02m, Time 1.00E00s, Velocity 3.00E-01m/s

DOI: 10.6041/j.issn.1000-1298.2025.05.029

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

12. Crop Sample Expansion and Fine Remote-sensing Recognition Using NDVI Time-series Characteristics

Accession number: 20252118492073

Title of translation: 基于NDVI时序特征的作物样本扩充与遥感精细识别

Authors: Bai, Yanying (1, 2); Yang, Ronghua (1, 2); Wang, Huiyong (3); Liu, Hui (4)

Author affiliation: (1) College of Water Conservation and Civil Engineering, Inner Mongolia Agricultural University, Hohhot; 010018, China; (2) State Key Laboratory of Water Engineering Eeology and Environment in Arid Area, Inner Mongolia Agricultural Vniversity, Hohhot; 010018, China; (3) Water Supply and Drainage Management Office of Inner Mongolia Hetao Irrigation District Water Conservancy Development Center, Bayannur; 015001, China; (4) Yichang Brauch Center of Inner Mongolia Hetao Irrigation District Water Conservancy Development Center, Bayannur; 015001, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 370-383

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The improvement of crop remote sensing identifieation accuracy is a key driving force for the leapfrog development of precision agriculture and smart agriculture. The accuracy of crop remote sensing identifieation depends on three elements: samples, image features and Classification methods. Aiming to reduce the Classification error caused by the bottleneck of sample data, the accuracy of crop remote sensing identifieation by jointly enhancing the sample quantity and quality control was improved. Taking the Wulanbuhe Irrigation District in the Hetao Irrigation Area as the study area, the time-series image of NDVI during the crop growth period in 2023 was construeted. Combined with the NDVI time-series characteristics of the crops, sampling was condueted on the image to expand the number of crop samples, and then the unqualified samples were screened and removed to achieve sample quality control. A total of 801 pixels of field samples (pre-expansion samples), 17 917 pixels of image samples (expanded samples), and 18 718 pixels of total samples (post-expansion samples) were selected. Four machine learning classifiers were used to compare the crop Classification effects before and after sample expansion. The results showed that the Classification accuracy of crops was significantly improved after sample expansion, with the Overall Classification accuracy increased by approximately 5 percentage points and the Kappa coefficient rose by about 0.05. Among them, the Classification accuracy of RF and NNC was relatively high, while that of CART and SVM was slightly lower. The crop remote sensing recognition was carried out after sample expansion by using CNN and LSTM deep learning models. The results showed that the Classification accuracy of CNN and LSTM was higher than that of RF and NNC, which had relatively high Classification accuracy. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Harvesters

Controlled terms: Agricultural implements - Damage detection - Model predictive control

Uncontrolled terms: Classification accuracy - Crop classification - Driving forces - NDVI time series - NDVI time-series characteristic - Precision Agriculture - Remote-sensing - Sample expansion - Smart agricultures - Time series characteristic

Classification code: 731.1 Control Systems - 821.2 Agricultural Machinery and Equipment - 913.3.1 Inspection - 1201.7 Optimization Techniques

DOI: 10.6041/j.issn.1000-1298.2025.05.036

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

13. Binocular Matching Method for Strawberry Targets Based on Semantic Features

Accession number: 20252118472007

Title of translation: 基于语义特征的草莓目标双目匹配方法研究

Authors: Bi, Song (1); Zhang, Donghang (1)

Author affiliation: (1) College of Electrical and Control Engineering, North China Vniversity ofTechnology, Beijing; 100041, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 415-424

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The binocular oamera-based target localization System has the advantages of short starting distanoe, high accuracy, and low cost, which is suitable for strawberry target identification and localization applications in space-constrained greenhouse strawberry production environments. Accurate target matching is the guarantee of the effectiveness of binocular camera measurements, but the surface brightness and shadow areas of strawberries vary greatly in natural environments, and it is difficult to obtain stable and accurate matching results with the binocular matching method based on local features. A binocular strawberry target matching method was investigated based on image semantic features, which can maintain the stability of the target description under the conditions of large illumination changes, rieh image texture, fruit occlusion, image blurring, etc., and therefore can improve the accuracy of binocular strawberry target matching. The semantic feature extraction method of the strawberry target region in the image was firstly designed, and secondly the strawberry target similarity calculation method was designed based on the semantic features and the geometric constraints of the binocular strueture, and finally the binocular strawberry target matching in the greenhouse environment was realized. The experimental results showed that the correct rate of strawberry target matching applied to the greenhouse environment by the method was 96. 3%, which can provide good target matching results for the strawberry target binocular localization System under the actual picking environment. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Agricultural implements

Controlled terms: Fruits - Harvesters - Semantic Segmentation

Uncontrolled terms: Binocular matching - Greenhouse environment - High-accuracy - Localisation Systems - Matching methods - Picking robot - Semantic features - Strawberry picking robot - Target localization - Target matching

Classification code: 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products - 1106.8 Computer Vision

Numerical data indexing: Percentage 3.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.039

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

14. Optimization Design and Experiment of Pneumatic Seeding Downforce Regulating Device

Accession number: 20252118472009

Title of translation: 气动式播种下压力调节装置优化设计与试验

Authors: Cao, Xinpeng (1, 2); Wang, Chen (1, 2); Peng, Chen (1, 2); Wang, Jinxing (1, 2); Zhang, Hongjian (1, 2); Sun, Linlin (1, 2)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China; (2) Shandong Provincial Key Laboratory of Horticultural Machinery and Equipment, Taian; 271018, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 257-267

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A pneumatic seeding downforce regulating device was designed to reduce pressure fluctuations caused by surface undulations during mechanized sowing Operations, which reduced the stability of seeding depth. The motion process of downforce regulating device was analyzed, and the torsional deformation process of the air spring which was the main working component was clarified. The main structural parameters of the air spring that affected the seeding downforce stability were determined through analyzing the influencing factors of seeding downforce and the deformation process of the air spring, including cord angle, piston radius, and piston angle. In order to determine the optimal parameter combination, a finite element Simulation model for gas-solid coupling of air Springs was established. Taking improving the downforce stability as the optimization index, a quadratic rotation orthogonal combination Simulation experiment was conducted, and a regression model of test indicators and influencing factors was established. Following the principle of reducing the vertical stiffness and ensuring that the vertical Output downforce of the air spring met the requirements, the optimal parameter combination for the downforce air spring were determined by Simulation experiment; cord angle was 38°, piston radius was 42 mm, piston angle was 23°. To verify the effectiveness of theoretical analysis and Simulation experiments, the field experiments were conducted on the pneumatic seeding downforce regulating device under the optimal parameter combination, the experimental results showed that the pneumatic seeding downforce regulating device can effectively improve the stability of ditch depth compared with the spiral spring seeding downforce regulating device. When the operating speedwas 4 km/h, 8 km/h, and 12 km/h, the qualified rates of ditch depth were increased by 8, 3 and 11 percentage points respeetively, reducing the eoefficient Variation of ditch depth by an average of 2. 58 percentage points, which significantly improved the consistency of seeding depth during mechanized seeding. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Pneumatics

Uncontrolled terms: Airsprings - Deformation process - Downforce regulating device - Optimal parameter combinations - Optimization design - Percentage points - Pressure fluctuation - Seeding - Seeding depth - Surface undulation

Classification code: 1401.3 Pneumatics, Equipment and Machinery

Numerical data indexing: Size 1.20E 04m, Size 4.00E 03m, Size 4.20E-02m, Size 8.00E 03m

DOI: 10.6041/j.issn.1000-1298.2025.05.024

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

15. Dynamic Predictions of Cotton Growth and Yield in Xinjiang Based on APSIM Model

Accession number: 20252118472008

Title of translation: 基于APSIM的新疆棉花生长与产量动态预测方法

Authors: Chen, Baiqing (1, 2); Zhang, Yue (3); Wang, Ke (1); Lü, Zhiyi (1); Chen, Maoguang (1); Tang, Qiuxiang (1)

Author affiliation: (1) College of Agronomj, Xinjiang Agrieultural University, Urumqi; 830052, China; (2) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (3) College of Agronomy, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 82-90

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A process-based cotton growth model could precisely and dynamically simulate the biomass accumulation and yield formation of cotton, so as to provide technical support for smart agrieultural decision-making. A dynamic prediction method for cotton growth and yield was developed by integrating meteorological data with the APSIM — Cotton model. Firstly, model parameters were calibrated based on field trial data (2023—2024). Secondly, short-term weather forecasts (ECMWF Open Data) were incorporated for 9 d growth simulations. Thirdly, climate analogue years were used to construet seasonal meteorological datasets to enable the dynamic yield prediction throughout the growing season of cotton. The results showed that the APSIM — Cotton model could accurately simulate the phenology dates (NRMSE was 5. 18%), biomass (NRMSE was 19. 60%), and yields (NRMSE was 6. 08%) of cotton under various planting densities (9-27 plants/m) in Changji, Xinjiang. Short-term biomass predictions achieved the highest aecuraey within 1 ~ 3 d (NRMSE was 1.3%), then the errors were increased to about 3. 24% at a 9 d forecast. Integrated meteorological data (the dynamic integration of historical meteorological data, short-term weather forecasts, and historical climate analog year data) enabled seasonal yield prediction. Using 18 optimal analogue years minimized prediction errors, stabilizing yield forecast errors below 4%. However, prediction accuracy fluctuated significantly between 90 d and 115 d after sowing (maximum relative error was 10%), which necessitated cautious application of the prediction results during this period. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Cotton

Controlled terms: Agricultural implements - Combines - Harvesters

Uncontrolled terms: APSIM — cotton model - Cotton growth - Cotton yield - Dynamic prediction - Growth and yield - Meteorological data - Weather data - Weather data fusion - Xinjiang - Yield

Classification code: 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products

Numerical data indexing: Percentage 1.80E 01%, Percentage 1.00E 01%, Percentage 1.30E 00%, Percentage 2.40E 01%, Percentage 4.00E 00%, Percentage 6.00E 01%, Percentage 8.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.008

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

16. Design and Experiment of Mobile Seed Corn Husker

Accession number: 20252118472001

Title of translation: 移动式制种玉米剥皮机设计与试验

Authors: Chen, Junzhi (1); Shi, Ruijie (1); Dai, Fei (1); Zhao, Wuyun (1); Chang, Leilei (1); Yang, Ting (2)

Author affiliation: (1) College of Mechanieal and Electrieal Engineering, Gansu Agricultural University, Lanzhou; 730070, China; (2) Jiuquan OK Seed Machinery Co., Ltd., Jiuquan; 735000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 331-342

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Ainring at the current domestic mechanized peeling of seed eorn using ordinary com combine harvester Operation with large kernel loss, high cost of peeling production line construetion, shortage of drying field peeling equipment and other problems, a mobile seed corn husker was designed, using flexible conveyor belt feeding device, füll rubber segmented combination peeling rollers, gas-solid eoupling type rapid Screening device and separable mobile frame design, in order to improve the efficiency of peeling, reduce kernel damage, and facilitate transport and storage. Through theoretical analyses and calculations on the peeling process of seed corn, the main factors affecting the peeling effect and the structural parameters of key components were determined. The Box — Behnken experimental design principle was adopted to conduct a three-factor, three-level experiment by using peeling roller rotational speed, peeling roller inclination angle and peeling roller bias angle as experimental factors, and bract Stripping rate, kernel shedding rate and kernel breakage rate as Performance indexes, and the verification test was carried out according to the actual working conditions at the end. The results of multi-objective optimization showed that the optimal combination of working parameters was the peeling roller rotational speed of 265. 57 r/min, the peeling roller inclination angle of 10.06°, and the peeling roller offset angle of 19. 76°,at which time the bract Stripping rate was 95. 33%, kernel shedding rate was 1. 471%, and the kernel breakage rate was 0. 661%. The results of the Validation test showed that at the optimal parameter combination of the bract Stripping rate was 94. 22%, the kernel shedding rate was 1.511%, and the kernel breakage rate was 0. 675%, which was basieally eonsistent with the results of the parameter optimization, meeting the operational requirements for mechanized peeling of seed com.The research result can be used for the design and improvement of seed corn husker to provide a reference. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 24

Main heading: Rollers (machine components)

Uncontrolled terms: ‘current - Breakage rates - Husker - Inclination angles - Low-loss - Low-loss flexible peeling - Mobile - Rotational speed - Seed corns - Stripping rate

Classification code: 601.2 Machine Components

Numerical data indexing: Angular velocity 9.519E-01rad/s, Percentage 1.511E 00%, Percentage 2.20E 01%, Percentage 3.30E 01%, Percentage 4.71E 02%, Percentage 6.61E 02%, Percentage 6.75E 02%

DOI: 10.6041/j.issn.1000-1298.2025.05.031

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

17. Review of Research Progress on Tropical Fruit Harvesting Robots

Accession number: 20252118471994

Title of translation: 热带水果采摘机器人研究进展综述

Authors: Chen, Tianci (1, 2); Zhang, Shiang (3); Fu, Genping (4); Chen, Jiazheng (1); Zhu, Lixue (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou; 510225, China; (2) College of Engineering, South China Agrieultural University, Guangzhou; 510642, China; (3) College of Innovation and Entrepreneurship Education, Zhongkai University of Agriculture and Engineering, Guangzhou; 510225, China; (4) College of Automation, Zhongkai University of Agriculture and Engineering, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 184-201

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Tropical fruits are significant export products for many tropical and subtropical countries, playing a crucial role in regional economic development. With the continuous advancement of agrieultural intelligent equipment and the Integration of emerging technologies such as artificial intelligence, machine learning, and Computer vision, automated and intelligent tropical fruit harvesting robots have gradually become a research hotspot in the field of agrieultural harvesting. The application and research progress of harvesting robots in the tropical fruit industry was comprehensively reviewed, and the current Status of tropical fruit harvesting both domestically and internationally was outlined. It highlighted the growing demand for efficient and sustainable harvesting Solutions due to labor shortages and increasing produetion costs. The technical characteristics of harvesting machinery for different tropical fruits were analyzed, such as bananas, pineapples, and mangoes, based on the complexity of their growing environments and the uniqueness of crop growth patterns. The key technologies of various Subsystems in tropical fruit harvesting robots were also discussed, including fruit identification and localization, robotic arm manipulation, and intelligent control Systems. The current technical challenges were examined, such as improving recognition aecuraey in complex environments and enhancing adaptability to different fruit varieties. Finally, it was looked forward to the opportunities presented by high-tech advancements such as artificial intelligence, big data analytics, and IoT in empowering agrieultural equipment. It proposed that future tropical fruit harvesting robots would increasingly move towards intelligent and unmanned harvesting, with potential applications in precision agriculture and smart farming Systems. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 154

Main heading: Harvesters

Controlled terms: Agricultural implements - Agricultural robots - Fruits - Robot learning

Uncontrolled terms: Crop characteristic - Emerging technologies - Export products - Fruits harvesting robots - Harvesting robot - Intelligent equipment - Regional economic development - Subtropical countries - Tropical countries - Tropical fruits

Classification code: 731.5 Robotics - 731.6 Robot Applications - 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products

DOI: 10.6041/j.issn.1000-1298.2025.05.018

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

18. Design and Test of Seeding Measurement and Control System for Cotton Precision Directing Machine Based on Radar Speed Measurement

Accession number: 20252118472023

Title of translation: 基于雷达测速的棉花精量直播机排种测控系统设计与试验

Authors: Ding, Youchun (1, 2); Zhang, Chihai (1, 2); Dong, Wanjing (1, 2); Zhan, Zhenyu (1, 2); Wei, Song (1, 2); Nong, Feng (1, 2)

Author affiliation: (1) College of Engineering, Huazhong Agrieultural Univerdty, Wuhan; 430070, China; (2) Key Laboratory of Agrieultural 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 59-70

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of sticky soil in the cotton area after wheat in the Yangtze River basin, large amount of straw stubble left on the ground surface, the ground wheel driving method is prone to winding slippage and congestion phenomenon, which leads to leakage of sowing, broken Strips, and the lack of integrated device for cotton seed guiding and Performance detection, thus a radar speed measurement based seeding measurement and control System of cotton precision direct seeding machine was designed. The cloud — end architecture model of 3-channel independent DC motor speed control and 3-channel real-time detection of cotton seed flow was constructed; the seeding measurement and control cloud platform was designed to realize real-time collection, display, storage and multi-device control of the seeding Operation parameters; and the fuzzy PID Controller was designed to realize real-time regulation and control of the specified grain spacing at the same time with the speed. Matlab was utilized to carry out a Simulation comparison test between the fuzzy PID Controller and the PID Controller. The results showed that the fuzzy PID algorithm model reduced the overshooting amount by 28. 95%, the rise time by 28. 57%, and the steady State time by 22. 22% than the PID algorithm model. The motor control accuracy test was carried out, and the results showed that the maximum speed error between the actual motor speed and the theoretical speed was 1. 8%, and the average error was 1.1%. The results of the bench test showed that compared with the JPS — 16 Computer vision test bench, the difference between the qualified index, Omission index and reseeding index detected by the measurement and control System was lower than 1.3, 0. 7 and 0. 8 percentage points, respectively. The results of the road test showed that the maximum error between the actual and theoretical number of seed rows was 2. 4% at the operating speed of 3. 6 ~9. 2 m/h; the accuracy of single-road detection was not less than 97. 44%. The results of field test showed that at the operating speed of 3. 6 ~ 9. 2 m/h, the seeding qualifieation index controlled by this System was higher than 90. 81%, the leakage index was lower than 4. 64%, and the coefficient of Variation of grain spacing was lower than 14. 38%. Demonstration application showed that this cotton precision direct seeding maehine row seeding measurement and control System can effectively improve the quality of cotton direct seeding machine sowing after wheat in the cotton area of Yangtze River Basin. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: AC motors

Controlled terms: Controllability - Parameter estimation - Time measurement

Uncontrolled terms: Algorithm model - Cotton seeds - Direct-seeding - Fuzzy PID controller - Fuzzy-PID - Measurement and control systems - Radar speed measurement - Seeding detection - Speed measurement - Yangtze River basin

Classification code: 705.3.1 AC and DC Motors - 731 Automatic Control Principles and Applications - 942.1.7 Special Purpose Instruments - 1201 Mathematics - 1202 Statistical Methods

Numerical data indexing: Percentage 1.10E 00%, Percentage 2.20E 01%, Percentage 3.80E 01%, Percentage 4.00E 00%, Percentage 4.40E 01%, Percentage 5.70E 01%, Percentage 6.40E 01%, Percentage 8.00E 00%, Percentage 8.10E 01%, Percentage 9.50E 01%, Size 2.00E 00m

DOI: 10.6041/j.issn.1000-1298.2025.05.006

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

19. Effects of Biochar and Straw on Transport of Water, Heat and Salt in Freeze-thawed Soil in Farmland

Accession number: 20252118477583

Title of translation: 生物炭和秸秆影响农田冻融土壤水热盐运移机理分析

Authors: Fu, Qiang (1, 2); Chen, Xuyang (1, 2); Li, Tianxiao (1, 2); Hou, Renjie (1, 2)

Author affiliation: (1) School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin; 150030, China; (2) Key Lahoratory for High Effective Utilization of Agricultural Water Resources, Ministry of Agriculture and Rural Affairs, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 501-511

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: During freeze-thaw cyeles, significant migration of soil moisture, heat, and salts occurs, which exaeerbates soil salinization, thereby having a profound impact on agricultural production stability and the sustainability of soil fertility. Based on field experiments, biochar and straw were applied to the 0 ~ 15 cm soil layer (BQ and CQ) and the 15 ~ 30 cm layer (BS and CS), with a blank control group (CK) as a comparison. The moisture content, temperature, and salt concentration in the 0-15 cm, 15 ~ 30 cm, and 30 ~ 45 cm layers were monitored during the freeze-thaw period to investigate the effects of biochar and straw applied at different depths on soil moisture, heat, and salt dynamics. A structural equation model was used to analyze the relationships between moisture, temperature, and salts across different soil layers. The results showed that during the experimental period, the application of biochar and straw significantly improved the water, heat, and salt characteristics of the soil. Specifically, the average moisture content in the 0 ~ 45 cm soil layer for the BQ, BS, CQ, and CS treatments was increased by 2. 85, 3. 13, 1.56, and 2. 15 percentage points, respectively, compared with that of the control group. All treatments effectively increased soil temperatures and reduced temperature fluctuations during the freeze-thaw period. The average salt concentration in the 0-45 cm soil layer for the BQ and BS treatments was increased by 0. 34 g/kg and 0. 40 g/kg, respectively, compared with that of the control group. Furthermore, the application of biochar effectively suppressed salt migration by adsorbing salts.The structural equation model results indicated that moisture migration affected both heat transfer and solute movement, and the application of bioehar and straw ehanged the correlations between water, heat, and salts across the different soil layers. These findings can provide theoretical and technical Support for regulating soil eeological environments in regions with seasonal frozen soil. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Main heading: Soil moisture

Controlled terms: Agricultural implements - Electric heating - Harvesters - Orchards - Permafrost - Radiant heating - Water content

Uncontrolled terms: %moisture - Biochar - Bioehar - Farmland freeze-thaw soil - Freeze/thaw - Heat - Salt migration - Soil layer - Soil salts - Structural equations

Classification code: 303.1 Process Heating - 304.1 Space Heating - 483.1 Soils and Soil Mechanics - 821.2 Agricultural Machinery and Equipment - 821.4 Agricultural Methods - 941.6 Moisture Measurements

Numerical data indexing: Mass 3.40E-02kg, Mass 4.00E-02kg, Size 0.00E00m to 1.50E-01m, Size 0.00E00m to 4.50E-01m, Size 1.50E-01m to 3.00E-01m, Size 3.00E-01m to 4.50E-01m

DOI: 10.6041/j.issn.1000-1298.2025.05.048

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

20. Effects of Combined Application of Silicon Fertilizer and Biochar on Phytolith Carbon Sequestration and Soil Greenhouse Gas Emissions in Black Soil Maize Region

Accession number: 20252118477642

Title of translation: 硅肥生物炭联合施用对黑土区玉米植硅体固碳与土壤温室气体排放的影响

Authors: Fu, Qiang (1, 2); Miao, Jiawei (1, 2); Li, Tianxiao (1, 2); Hou, Renjie (1, 2)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 512-522

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The accumulation of phytolith-occluded organie earbon (PhytOC) in soil is a potential pathway for long-term organie earbon Sequestration. Silieon fertilizer, an exogenous Silicon amendment, can enhance earbon Sequestration by phytoliths in crops, while biochar plays an active role in redueing soil greenhouse gas emissions. To investigate the pathways through which combined applieation of Silicon fertilizer and biochar affected the earbon Sequestration capaeity of phytoliths and its impact on soil greenhouse gas emissions, four treatments were established, including control (CK), Silicon fertilizer (SF), biochar (BC), and a mixed applieation of Silicon fertilizer and biochar (BS). The distribution characteristics of soil Silicon fractions were analyzed through field experiments combined with laboratory phytolith stability grading tests to confirm differences in PhytOC Sequestration and its stability in different maize organs. Additionally, the effeets of mixed exogenous Silicon on crop agronomic characteristics and the reduetion of soil greenhouse gas emissions were elueidated. The results indieated that under the BS treatment, the Contents of readily soluble Silicon (CaCl2 — Si), unstable Silicon on the surface of inorganic soil particles (Acetic — Si), and unstable Silicon on the surface of soil organic matter (H202 -Si), exhibited an initial increase followed by a decrease, while the content of weakly crystalline Silicates and amorphous Silicon (Na2C03—Si) showed a continuous decline. BS treatment significantly increased the phytolith content of the maize stem, sheath, and leaf by 54.75%, 5. 68%, and 56.87%, respectively, compared with CK. The crop PhytOC production flux reached 57.79 kg/(hm *a). The stable phytolith content was increased by 16. 32 percentage points, and the stable PhytOC production flux reached 34. 12kg/(hm *a). Furthermore, the global warming potential (GWP) under combined application was 3 716. 88 kg/hm. The results demonstrated that combined application of Silicon fertilizer and biochar significantly enhanced the carbon Sequestration capacity of crop phytoliths and reduction of soil greenhouse gas emissions, providing strategies and methodologies for achieving long-term carbon Sequestration. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Harvesters

Controlled terms: Agricultural implements - Carbon emissions - Combines - Garnets - Greenhouse gas emissions - Organoclay - Phytoplankton - Quartz

Uncontrolled terms: Biochar - Black soil - Carbon sequestration - Distribution characteristics - Exogenous silicon - Greenhouse gas emissions - Mixed exogenous silicon - Phytoc sequestration - Phytolith - Silicon fraction

Classification code: 103 Biology - 482.1 Minerals - 482.1.1 Gems - 821.2 Agricultural Machinery and Equipment - 1502.1.2 Climate Change

Numerical data indexing: Mass 5.779E 01kg, Mass 8.80E 01kg, Percentage 5.475E 01%, Percentage 5.687E 01%, Percentage 6.80E 01%, Mass 1.20E 01kg

DOI: 10.6041/j.issn.1000-1298.2025.05.049

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

21. Deviate Regulation Method of Hydraulic Excitation System Controlled by Alternating Flow Distribution Pump

Accession number: 20252118477992

Title of translation: 交变配流泵控液压激振系统偏移调控方法研究

Authors: Ge, Zheng (1); Ren, Yu (1); Li, Xiang (1); Wang, Xianyan (2)

Author affiliation: (1) School of Information Science and Engineering, School of Cyber Science and Technolgy), Zhcjiang Sci-Tech University, Hangzhou; 310018, China; (2) Ruili Croup Ruian Auto Parts Co., Ltd., Wenzhou; 325200, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 608-616

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The alternating flow distribution pump ean directly Output alternating fluid flow through the continuous rotation of its built-in rotating valve plate, driving the hydraulic cylinder to generate excitation motion. Due to nonlinear factors such as internal leakage and friction within the alternating flow distribution pump and pump-controlled hydraulic cylinder, the bidirectional fluid flow generated by the pump may not be entirely symmetrical. As a result, the displacement center of the pump-controlled hydraulic cylinder may shift during the excitation process, thereby affecting its practicality. To address above problem caused by asymmetric fluid flow, a pump — valve — cylinder cascaded deviate regulation System was designed by placing a servo valve in series between the alternating flow distribution pump and the hydraulic cylinder. Additionally, a half-cycle piecewise deviate regulation control strategy based on the phase of the rotating valve plate was proposed. Based on the dynamic characteristics of the pump and valve, an AMESim — Simulink co-simulation model was established. System parameters were identified by experiment. The control Performance was analyzed through Simulation. A test platform was constructed to verify the effectiveness of the deviate regulation method. The research result showed that this method can effectively control the displacement center of excitation hydraulic cylinder controlled by the alternating flow distribution pump. Moreover, the servo valve remained mostly at a large opening, minimizing throttling losses. This approach ensured the high efficiency of the pump-controlled excitation System while improving its practicality. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Stream flow

Controlled terms: Hydraulic drives - Rotating machinery - Servomotors - Three term control systems

Uncontrolled terms: Alternating flow distribution pump - Deviate regulation - Excitation system - Flow distribution - Fluid-flow - Fuzzy-PID - Hydraulic cylinders - Leakage - Servo-valve - Valve plates

Classification code: 301.1.1 Liquid Dynamics - 601.1 Mechanical Devices - 602.1 Mechanical Drives - 705.3 Electric Motors - 731.1 Control Systems - 731.7 Mechatronics - 1401.2 Hydraulic Equipment and Machinery - 1502.3 Hydrology

DOI: 10.6041/j.issn.1000-1298.2025.05.059

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

22. Numerical Prediction Analysis of Cavitation Erosion of Hydrofoils Considering Energy Transfer Efficiency

Accession number: 20252118477645

Title of translation: 考虑能量传递效率的水翼空蚀数值预测分析

Authors: Geng, Linlin (1, 2); Fang, Haiyuan (2); Cao, Yantao (3); Zheng, Enhui (3); Li, Ning (1)

Author affiliation: (1) Science and Technology on Water Jet Propulsion Laboratory, Marine Design and Research Institute of China, Shanghai; 200111, China; (2) Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang; 212013, China; (3) Key Laboratory on Ship Vibration and Noise, China Ship Scientific Research Center, Wuxi; 214082, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 361-369

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurately predicting the erosion regions is always a challenging point of numerical cavitation erosion Simulation, which is beneficial for designing and extending the lifespan of the hydraulic machinery. The density-eorreeted SST k — co turbulence model and the Sauer cavitation model were used to simulate the unsteady cavitation around NACA0009 3D twisted hydrofoil. The aceuracy of the current numerical method was verified by comparing the cavitation shedding frequency and transient cavity behaviors in the experiment. Considering the energy transfer efficiency, a propagation relationship of cavitation energy from the flow field Space radiation to the wall was constructed, thereby predicting the wall erosion load. By comparing the erosion energy and erosion load on the hydrofoil surface at different instants, it was found that compared with cavitation energy, the erosion load, which comprehensively considered the influence of erosion energy from the whole flow field on hydrofoil surface, predicted a wider coverage erosion area. Moreover, the average wall surface erosion intensity distribution was obtained. By time-averaging the wall surface erosion intensity solved from each instantaneous time step within 12 cycles, and by comparing the average wall surface erosion intensity obtained from the erosion energy and erosion load with the experimental erosion results, it was demonstrated that the erosion area predicted by time-averaged erosion load was more agreeable to the experiment result, indicating that it was necessary to consider the energy transfer efficiency when predicting cavitation erosion. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Cavitation

Uncontrolled terms: Cavitation energy - Energy - Energy transfer efficiency - Erosion energy - Erosion intensity - Erosion load - Hydrofoil surfaces - Naca0009 3d twisted hydrofoil - Surface erosion - Wall surfaces

Classification code: 301.1.1 Liquid Dynamics

DOI: 10.6041/j.issn.1000-1298.2025.05.034

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

23. Discrete Element Model Construction and Parameter Calibration of Combined Harvest Oil Sunflower Extract

Accession number: 20252118471995

Title of translation: 联合收获油葵脱出物离散元模型构建与参数标定

Authors: Guo, Hui (1, 2); Han, Junxuan (1); Lu, Zengshuai (3); Dong, Yuande (1, 2); Guo, Liehong (1); Zhou, Wen (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi; 830052, China; (3) Institute of Crop Research, Xinjiang Academy of Agricultural Reclamation Sciences, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 319-330

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address lack of accurate modeling in discrete element Simulation analysis for cleaning devices in combined oil sunflower harvest, the combined-harvested oil sunflower extracts were taken as object. A discrete element method was used to categorize and calibrate contact parameters for various oil sunflower extract models. The randomly selected oil sunflower extracts were classified, its main components were identified, and the corresponding mass fractions were determined using digital calipers, a universal testing machine, and a custom test platform measure intrinsic and contact parameters of each oil sunflower extract. Plackett — Burman, the steepest ascent, and Box — Behnken tests were proceeded based on each extract’ s physical repose angle. Parameters with significant effects on extract repose angle were identified and their valid ranges were defined. An optimization module in Design-Expert Software was employed and physical repose angle of each extract was treated as the objective value. The optimal parameter sets were determined as follows; oil sunflower seed shear modulus was 7.35 X 10 Pa, oil sunflower seed — steel restitution coefficient was 0.295, oil sunflower seed — oil sunflower seed static friction coefficient was 0. 669, crushed oil sunflower head shear modulus was 1. 94 X 10 Pa, crushed oil sunflower head — steel restitution coefficient was 0. 467, crushed oil sunflower head — steel static friction coefficient was 0. 436, oil sunflower stalk shear modulus was 7. 39 X 10 Pa, oil sunflower stalk — steel static friction coefficient was 0. 553, and oil sunflower stalk — oil sunflower stalk static friction coefficient was 0.775. Simulation stacking tests were conducted on oil sunflower seeds, crushed oil sunflower heads, stalks, and mixed extracts based on each optimal parameter set. Results showed that errors between simulated and physical repose angles were 0. 66%, 0. 96%, 0. 64% and 1. 15%, respectively. These findings can serve as a reference for diserete element Simulation research of combined oil sunflower harvest. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Stiction

Uncontrolled terms: Contact parameters - Diserete element - Eombine harvest - Extraction eomponent - Optimal parameter - Parameters calibrations - Repose angles - Static friction coefficient - Sunflower seeds - Sunflower stalks

Classification code: 601 Mechanical Design - 606 Lubrication and Tribology

Numerical data indexing: Percentage 1.50E 01%, Percentage 6.40E 01%, Percentage 6.60E 01%, Percentage 9.60E 01%, Pressure 1.00E 01Pa

DOI: 10.6041/j.issn.1000-1298.2025.05.030

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

24. Design and Experiment of Crawler Walking System of Cotton Topping Machine

Accession number: 20252118472018

Title of translation: 棉花打顶机履带行走系统设计与试验

Authors: Han, Changjie (1, 2); Li, Chao (1); Xu, Yang (1); Qiu, Shilong (1); Luo, Yan (1); You, Jia (1); Mao, Hanping (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Xinjiang Intelligent Agricultural Machinery Equipment Engineering and Technology Research Center, Urumqi; 830052, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 121-129

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 severe shaking and poor stability of the topping device in the process of cotton topping Operation, aceording to the planting mode of machine-picked cotton field in Xinjiang region, the mechanically-picked cotton field was selected as the research object, a crawler Walking System of cotton topping machine based on Beidou navigation was designed, and the design structure and parameters of the chassis and key components were determined. STM32F103 was used as the main Controller, equipped with a real-time dynamic difference Beidou navigation System to realize automatic navigation control, and the kinematic model of the tracked chassis was established to determine the preview point, and the curvature deviation between the positioning point and the target path was obtained, at the same time, the speed of the chassis geometrio center was introduced as the input of the Controller, and a self-adjusting double-input fuzzy PID control algorithm was proposed to realize the automatic tracking of the operating path of the chassis. The results showed that the maximum absolute deviation was not more than 39 mm, and the Standard deviation was not more than 18. 5 mm, the average absolute deviation was 15.3 mm. The average heading angle deflection angle, the average roll angle deflection angle and the average pitch angle of the topping device were 0.38°, 0. 33° and 0.26°, respectively. The self-propelled crawler chassis had strong driving stability, accurate alignment and small tracking error, and can be applied to cotton topping Walking Operation. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Automatic guidance (agricultural machinery)

Controlled terms: Deflection (structures) - Inertial navigation systems - Vector control (Electric machinery)

Uncontrolled terms: Beidou navigation system - Cotton fields - Cotton topping machine - Deflection angles - Dual-motor crawler chassi - Dual-motors - Fuzzy-PID control - Path tracking - Poor stability - Walking systems

Classification code: 408.1 Structural Members and Shapes - 435.1 Navigation - 705.1 Electric Machinery - 731 Automatic Control Principles and Applications - 821.2 Agricultural Machinery and Equipment

Numerical data indexing: Size 1.53E-02m, Size 3.90E-02m, Size 5.00E-03m

DOI: 10.6041/j.issn.1000-1298.2025.05.012

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

25. Design and Experiment of Liquid-gas-assisted Cistanche deserticola Seeder

Accession number: 20252118471996

Title of translation: 气液辅助式肉苁蓉播种机设计与试验

Authors: Han, Changjie (1); Zhang, Yue (1); You, Jia (1); Liu, Yongping (2); Liang, Jia (3); Ma, Xu (1); Mao, Hanping (1)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Institute of Afforestation and Desertification Control, Xinjiang Academy of Forestry, Urumqi; 830052, China; (3) Bazhou Liangjia Agricultural Machinery Manufacturing Co., Ltd., Yanqi; 841100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 291-299

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Cistanche deserticola is a herbaceous plant that parasitizes deep in the roots of Haloxylon ammodendron and Tamarix ramosissima. Due to its small seed volume, high price, and the requirement that the seeds need to be attached near the roots of host plants during sowing, the mechanized sowing of Cistanche deserticola demands precise seed metering and deep sowing. Aecording to the agronomie planting requirements of Cistanche deserticola and combined with manual sowing methods, a Cistanche deserticola seeder capable of ditching, sowing, and eovering soil Operations was designed and manufactured. By analyzing the stress State of the ditching cutterhead, the main stmctural parameters and the blade layout pattern of the cutterhead were determined. Based on the principle of constant volume of soil throwing and eovering, the structural parameters of the soil guide cover were designed. Combined with finite element analysis Software, modal analysis was mainly carried out on the ditching cutterhead and the main drive shaft, and the working parameters of the machine were determined. The sowing flow rate of Cistanche deserticola was matched aecording to the liquid spraying volume and the working speed, and the form and size ränge of the seed outlet were determined. Taking the blowing air pressure at the nozzle and the opening degree of the seed spraying port as experimental factors, and the coefficient of Variation of sowing uniformity as the experimental index, field experiments were carried out. The results showed that when the blowing air pressure at the nozzle was 0. 2 MPa and the opening degree of the seed outlet was 1. 5 mm, the coefficient of Variation of sowing uniformity was 13. 50%, and the sowing depth stability coefficient was not less than 91. 17%, which met the current agronomie planting requirements of Cistanche deserticola. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Modal analysis

Controlled terms: Combines - Harvesters - Seed

Uncontrolled terms: Air-pressure - Blowing air - Cistanche deserticola - Coefficients of variations - Ditching seeder - Haloxylon ammodendron - Herbaceous plants - Liquid sowing - Opening degree - Plantings

Classification code: 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products - 1201.5 Computational Mathematics

Numerical data indexing: Percentage 1.70E 01%, Percentage 5.00E 01%, Pressure 2.00E 06Pa, Size 5.00E-03m

DOI: 10.6041/j.issn.1000-1298.2025.05.027

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

26. Obstacle Detection Method for Complex Cotton Field Environments Based on Improved YOLO lln Model

Accession number: 20252118472025

Title of translation: 基于改进YOLO 11n模型的棉花田间复杂环境障碍物检测方法

Authors: Han, Keli (1, 2); Wang, Zhenkun (1, 2); Yu, Yongfeng (1, 2); Liu, Shuping (2, 3); Han, Shujie (2, 3); Hao, Fuping (2, 3)

Author affiliation: (1) Chinese Academj of Agricultural Mechanization Sciences Group Co., Ltd., Beijing; 100083, China; (2) Modern Agricultural Equipment Co., Ltd., Beijing; 100083, China; (3) National Key Laboratory of Agricultural Equipment Technology, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 111-120

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of accurate obstacle detection in complex cotton field environments due to occlusions and the computational limitations of edge devices, a field obstacle detection method based on improved YOLO lln model was proposed. Firstly, the lightweight StarNet network was adopted as the primary feature extraction network, and the dynamic position bias attention block module (DBA) was introduced to reconstruct convolutional block with parallel spatial attention (C2PSA) to enhance multi-scale feature interaction. Secondly, Kolmogorov — Arnold generalized network convolution (KAGNConv) was used to replace the bottleneck structure in the cross stage partial with kernel size 2 module (C3k2) of the baseline model, enabling fine-grained feature extraction while improving model flexibility and interpretability. Finally, the separated and enhancement attention module (SEAM) was integrated into the detection head to enhance the model’s detection capability in occlusion scenarios. The experimental results showed that, compared with the baseline model, the improved YOLO lln — SKS achieved increases of 2. 3, 2. 1, 1.3, and 1. 4 percentage points in precision, recall, mAP5(), and mAP5()_95, reaching 91. 7%, 88. 3%, 91. 9%, and 62. 3%, respectively. The model’ s floating-point Operations were reduced to only 4.4 x 10 FLOPs, and the number of model parameters was deereased by 17. 1%. This study achieved a favorable balance between Performance and eomputational complexity, meeting the real-time detection requirements of cotton harvesting Operations while lowering the eomputational demands for deployment on edge devices, thereby providing technical Support for the autonomous and safe Operation of cotton pickers. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Edge detection

Controlled terms: Digital arithmetic - Image coding - Image segmentation

Uncontrolled terms: Baseline models - Computational limitations - Cotton fields - Cotton pickers - Depth camera - Detection methods - Features extraction - Objeet recognition - Obstacles detection - YOLO lln model

Classification code: 1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory - 1106.3.1 Image Processing - 1106.8 Computer Vision

Numerical data indexing: Percentage 1.00E00%, Percentage 3.00E 00%, Percentage 7.00E 00%, Percentage 9.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.011

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

27. Design and Experiment of Drum Type Fallen Peanut Picking Up Machine

Accession number: 20252118477584

Title of translation: 滚筒式花生落果捡拾机设计与试验

Authors: Hao, Jianjun (1, 2); Zhang, Ying (1); Li, Zhaowei (1); Yang, Shuhua (1); Han, Peng (3); Guo, Xiuyun (4)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding; 071001, China; (2) Technology Innovation Center of Intelligent Agricultural Equipment of Hebei Province, Baoding; 071001, China; (3) Hebei Agricultural Technology Extension Station, Shijiazhuang; 050011, China; (4) Luanzhou Baixin Peanut Planung Professional Cooperative, Luanzhou, 063799, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 343-352

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A drum type fallen peanut pieking up machine was designed to address the issues of excessive fallen peanut and difficulty in recycling after peanut harvest. The excavation and pieking device, upward conveying device, Screening and collecting device were designed and related parameters were calculated. The current research Status and existing problems of fallen peanut picking up machine were introduced, providing ideas and key Solutions for future design. Firstly, the entire machine was constructed to determine the main technical parameters of drum type fallen peanut picking up machine and introduce its working principle, then the key devices and parameters were designed and optimized. In the excavation and picking device, the excavation angle and shovel surface width of the excavation shovel were determined, and the structure and material design of the feeding roller and guide grid were carried out. The force analysis and kinematic analysis were conducted on the fallen peanut — soil mixture on the upward conveying device, and the theoretical value ränge of the angle between the upward conveying monomer and the upward conveying chain, as well as the operating speed of the upward conveying chain, were obtained. Through kinematic analysis of the fallen peanut — soil mixture in the Screening and collecting device, the diameter D of the roller screen, the pore width K of the screen mesh, the rotational speed n of the roller screen, and the installation angle of the roller screen were obtained. Through EDEM Simulation analysis, the angle between the upward conveying monomer and the upward conveying chain, speed of the upward conveying chain, the leakage and lifting Situation of two factors were observed. Using pick up rate and trash content as evaluation indicators, through single factor and double factor Simulation analysis of upward conveying device Operation parameters, the angle between the upward conveying monomer and the upward conveying chain, the running speed of upward conveying chain, and the impact of their interaction on peanut lifting efficiency and trash content were clarified. The optimal parameter eombination was obtained as follows; the angle between the upward conveying monomer was 45°, upward conveying ehain running speed was 1. 5 m/s. Aecording to the preliminary design, peanut varieties were pre-picked, and field tests were conducted after measuring the soil moisture content and the distribution of fallen peanut. The field test results of the prototype showed that the pick up rate of the whole machine was 95%, and the trash content was 8. 5%, which met the design requirements of the whole machine. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Rollers (machine components)

Controlled terms: Conveyors

Uncontrolled terms: Fallen peanut - Kinematic Analysis - Parameter optimization - Picking up - Picking up machine - Roller screen - Running speed - Simulation analysis - Soil mixtures - Upward conveying device

Classification code: 601.2 Machine Components - 692.1 Conveyors

Numerical data indexing: Percentage 5.00E 00%, Percentage 9.50E 01%, Velocity 5.00E 00m/s

DOI: 10.6041/j.issn.1000-1298.2025.05.032

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

28. UAV-driven 3D Spatiotemporal Canopy Modeling Enhanced High-accuracy Cotton Biomass Retrieval

Accession number: 20252118472010

Title of translation: 无人机冠层3D时序动态建模驱动棉花生物量高精度反演研究

Authors: Hu, Zhengdong (1, 2); Tang, Qiuxiang (1); Fan, Shiyu (1, 2); Bao, Longlong (1, 2); Guldana, Sarsen (1, 2); Lin, Tao (2)

Author affiliation: (1) College of Agronomy, Xinjiang Agrieultural University, Urumqi; 830052, China; (2) Cotton Research Institute, Xinjiang Uyghur Autononions Region Academy of Agrieultural Sciences, Urumqi; 830091, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 103-110

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aecurate above ground biomass (AGB) estimation is a key technology for crop growth monitoring and precision agriculture decision making. Aiming to address the two limitations of traditional unmanned aerial vehicle (UAV) remote sensing methods in cotton AGB estimation—models based on Vegetation indices (Vis) were susceptible to the interference of canopy spectral Saturation effects, and it was difficult to quantify the spatio-temporal heterogeneity of the dynamics of three-dimensional canopy strueture and AGB aecumulation—the spatial analysis of three-dimensional UAV point clouds and the temporal characteristics of canopy Cover were integrated to construet a multi-dimensional estimation model based on plant height X canopy cover (PH X CC). By designing a comparative experimental framework, the Performance differences between the PH x CC model and four types of traditional models were investigated: Vis combined with random forest (RF), gradient boosting (GB), support vector machine (SVM) and backpropagation neural network (BPNN) were systematically evaluated. The results showed that the PH X CC model had significant advantages on the test set. Its coefficient of determination of estimation aecuraey (R) was 0. 93, and the root mean Square error (RMSE) was 15. 30 g/m, which was an improvement of 22. 3% compared with that of the optimal traditional model (RF; R = 0. 76, RMSE was 23. 35 g/m) (P © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Cotton

Controlled terms: Agricultural implements - Agricultural robots - Backpropagation - Combines - Deforestation - Harvesters - Reforestation - Target drones

Uncontrolled terms: 3D models - 3d-modeling - Aboveground biomass - Aerial vehicle - Biomass estimation - Canopy cover - Estimation models - Machine learning algorithms - Model-based OPC - Vegetation index

Classification code: 652.1.2 Military Aircraft - 731.6 Robot Applications - 821.1 Woodlands and Forestry - 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products - 1101.2 Machine Learning - 1502.4 Biodiversity Conservation

Numerical data indexing: Linear density 3.00E-02kg/m, Linear density 3.50E-02kg/m, Percentage 3.00E 00%, Percentage 5.70E 01%, Percentage 8.30E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.010

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

29. Design and Test of Chain Spoon Cocked Tail Self-cleaning Pinellia ternata Precision Seed Metering Device

Accession number: 20252118472037

Title of translation: 链勺翘尾自清式半夏精量排种器设计与试验

Authors: Huang, Yuxiang (1, 2); Liu, Zhuotao (1); Yang, Xin (1); Feng, Tian (1); Bi, Yubin (1); Ju, Xiaoteng (1)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shannxi, Yangling; 712100, China; (2) Shaanxi Engineering Research Center for Agricultural Equipment, Shannxi, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 279-290

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of insufficient seed filling and difficult seed cleaning eaused by irregulär shape and uneven size of Pinellia ternata seeds, based on the characteristics of chain flipping motion, a method of using rotational inertia force for seed cleaning was proposed. A chain spoon upturned tail self-cleaning Pinellia ternata precision seeder was designed, which improved the seed filling rate and achieved rapid seed cleaning through the spoon upturned tail structure and water droplet shaped holes. By analyzing the force and motion State of seeds during the working process of the seeder, the working principle of the chain spoon seif cleaning seeder with upturned tail was explained. Through theoretical calculations and kinematic analysis, Simulation experiments were conducted based on DEM — MBD coupling to analyze the effects of different spoon filling angles, chain tension forces, and spoon shaped hole structural parameters on the Performance of the seeder. The structural parameters of the seeder were determined. A quadratic regression orthogonal rotation combination Simulation experiment was designed to determine the optimal structural parameter combination for spoon shaped holes; spoon shaped hole length was 18.6 mm, spoon shaped hole width was 14. 1 mm, and spoon shaped hole retention depth was 8. 6 mm. To determine the optimal operating parameters of the seeder, a quadratic regression orthogonal rotation combination bench test was conducted with the driving sprocket speed and seed layer height as experimental factors. The experimental results showed that when the driving sprocket speed of the seeder was 39. 2 r/min and the seed layer height was 206 mm, the operational Performance of the seeder was optimal, with a qualification index of 93.37%, a replay index of 2. 17%, and a leakage index of 4. 46%, the research results can provide a reference for the design of seeders for bulbous Chinese medicinal materials. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Sprockets

Controlled terms: Dynamic response - Model structures

Uncontrolled terms: Chain spoon type - Orthogonal rotations - Pinellia ternata - Precision seed-metering devices - Quadratic regression - Seed filling - Self cleaning - Self-cleaning type - Shaped holes - Structural parameter

Classification code: 601.2 Machine Components - 602 Mechanical Drives and Transmissions - 1201.12 Modeling and Simulation

Numerical data indexing: Percentage 4.60E 01%, Percentage 9.337E 01%, Size 1.00E-03m, Size 1.86E-02m, Size 2.06E-01m, Size 6.00E-03m, Angular velocity 3.34E-02rad/s, Percentage 1.70E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.026

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

30. Exploration of Intelligent Semantic Matching Technique for Agricultural Short Texts Utilizing Feature Enhancement

Accession number: 20252118477650

Title of translation: 基于特征增强的农业短文本语义智能匹配方法研究

Authors: Jin, Ning (1); Guo, Yufeng (1, 2); Qu, Li’na (1); Miao, Yisheng (2, 3); Wu, Huarui (2, 3)

Author affiliation: (1) School of Computer Science and Engineering, Shenjang Jianzhu University, Shenyang; 110168, China; (2) National Engineering Research Center for Information Technology in Agriculture, Beijing; 100097, China; (3) Key Lahoratory of Agricultural Information Technology, Ministry of Agriculture and Rural Affairs, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 395-404

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A deep learning model Font_MBAFF was proposed for the task of text similarity calculation, which was mainly applied to the matching of question pairs in Chinese agricultural short texts. In order to solve the problems of sparse semantic features and inadequate understanding of speeialized vocabulary in agricultural short texts, it was firstly optimized in the feature representation stage. By introducing the unique font features of Chinese characters to expand the features, including side radicals and four corner numbers, thus enriching the semantic representation of features. In the feature extraction layer, the multi-scale convolution attention Channel weighted network MSCN and the bidirectional long short-term memory network Multi_SAB based on multi-head self-attention mechanism were combined respectively, so that the model can further optimize the feature extraction from the spatial and temporal relationship sequences of semantic features. Finally, TEXTAFF, an improved attention fusion mechanism for text, was used in the intelligent fusion stage of features. The experimental results indicated that the Font_ MBAFF model can effectively compensate for the lack of feature words in short texts, optimizing text feature extraction and feature fusion. The accuracy of semantic matching reached 96. 42%. Compared with five other semantic matching models, including MaLSTM, BiLSTM, BiLSTM _ Seif — attention, TEXTCNN _ Attention, and Sentence — BERT, the Font _ MBAFF model demonstrated significant advantages, achieving a correctness rate that was at least 2. 07 percentage points higher. Furthermore, the model proved resilient in experiments with datasets of different sizes, showing rapid response times during testing. Font _ MBAFF deep learning model exceled at determining the similarity of Chinese agricultural short texts. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Deep learning

Controlled terms: Agricultural implements - Combines - Feature extraction - Harvesters

Uncontrolled terms: Agricultural short text - Feature representation - Features extraction - Glyph feature representation - Learning models - Matching techniques - Multi-feature fusion - Semantic features - Semantic matching - Short texts

Classification code: 821.2 Agricultural Machinery and Equipment - 1101.2 Machine Learning - 1101.2.1 Deep Learning

Numerical data indexing: Percentage 4.20E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.037

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

31. Planning of Rural Energy System Integrating Biomass and Solar Energy with Electrification of Agricultural Machinery

Accession number: 20252118477586

Title of translation: 考虑农机电能替代的生物质耦合太阳能农村能源系统规划

Authors: Jing, Chenhui (1); Zheng, Zongming (2); Hou, Hongjuan (1); Chen, Yuefeng (3); Duan, Ruonan (2); Yang, Xiao (4)

Author affiliation: (1) State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sourees, North China Electric Power University, Beijing; 102206, China; (2) Sehool of New Energy, North China Electric Power University, Beijing; 102206, China; (3) Zhejiang Brauch oj Chinese Acadcmy of Agricultural Mechanization Sciences Group Co., Ltd., Shaoxing; 312000, China; (4) 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 560-568

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Under the background of dual-carbon goals, energy transition in rural areas has become a critical element in achieving green development. Traditional agricultural machinery in rural China, such as tractors and harvesters, heavily relies on fossil fuels, necessitating a shift toward low-carbon and sustainable development models represented by electric agricultural machinery. This transition promoted the utilization of renewable energy and provided an environmentally friendly Solution for sustainable agricultural development. Considering the abundance of renewable resources like solar energy and biomass in rural areas, as well as the operational characteristics of agricultural machinery and flexible agricultural loads, a planning method for a rural energy System integrating biomass and solar energy with electrified agricultural machinery was proposed. Firstly, the framework of rural energy System considering electric farm machines was constructed, and the load of electric farm machines and the flexible load of agriculture were modeled and analyzed at the same time. Secondly, an optimization model was established with the objectives of minimizing economic costs and carbon emissions, and solved collaboratively by using the non-dominated sorting genetic algorithm II (NSGA — E) and the commercial solver Gurobi. Finally, a planning Simulation was carried out for a farm in Northeast China. Results showed that introducing electric agricultural machinery and agricultural flexible loads reduced total System costs by 24% and carbon emissions by 46% under the condition of considering the purchase of electric agricultural machinery. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Harvesters

Controlled terms: Agricultural implements - Agricultural robots - Combines - Milking machines - Solar fuels - Tractors (agricultural)

Uncontrolled terms: Agricultural flexible load - Electric agricultural machineries - Energy - Energy systems - Farm machines - Flexible loads - Integrated energy systems - Rural energy - Rural integrated energy system - System planning

Classification code: 731.6 Robot Applications - 821.2 Agricultural Machinery and Equipment - 1008.4 Solar Energy Conversion and Power Generation

Numerical data indexing: Percentage 2.40E 01%, Percentage 4.60E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.054

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

32. Design and Experiment of Flexible Belt Clamp Directional Discharging Device for Attium chinense

Accession number: 20252118472011

Title of translation: 柔性带式藠头夹持定向投种装置设计与试验

Authors: Kang, Qixin (1); Zhang, Guozhong (1, 2); Zhao, Zhuangzhuang (1); Liu, Wanru (1); Tang, Nanrui (1); Liu, Haopeng (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: 5

Issue date: May 2025

Publication year: 2025

Pages: 246-256

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The Allium chinense, a perennial plant of the Allium genus in the lily family, is predominantly found in the Yangtze River basin and southern regions of China, with a cultivation area exceeding 6. 6 x 10 hm. ft boasts a unique flavor and high medicinal and edible value, being extensively exported to eountries such as Japan and South Korea. It has become a characteristie industry and foreign exehange product for implementing the “Rural Revitalization” strategy in some areas, with broad market prospects. Currently, the cultivation of Allium chinense largely relies on manual labor, which is physically demanding and eostly, thus hindering the industry’s large-scale development. There is an urgent need to develop and apply mechanized planting equipment for Allium chinense. Aiming to address the agronomie specifications regarding the orientation of bud scales and the engineering requirements for low-position discharging during the mechanized planting of Allium chinense, a flexible belt clamp directional discharging device for Allium chinense, based on the spoon clip type seed metering device was engineered. The mechanism comprised a feed deflector, conveyor Systems, electric motors, synchronous pulleys, and flexible belts, among other components. The operational principle of the flexible belt clamp seed discharging device was subjected to theoretical analysis, with a focus on the feeding, seed posture correction, and seed discharging processes. This analysis facilitated the determination of the structural design and parametric specifications for critical components. A coupled Simulation model was constructed, integrating multi-flexible body dynamics (MFBD) and the discrete dement method (DEM). Using response such as the seed horizontal discharging rate, horizontal seeding rate, and qualified rate of hole distanee, a significance Screening of the coupled Simulation experiments was performed, considering five key factors: the clamp belt angle, theoretical conveying speed, belt speed differential ratio, clamp belt spacing, and feeding radius. Subsequently, a regression orthogonal field test was executed, focusing on the clamp belt angle, theoretical conveying speed, belt speed differential ratio as experimental variables. Employing the Plackett — Burman design and the Box — Behnken central composite design, regression models were formulated for the seed horizontal discharging rate and the qualified rate of hole distanee. These models were then utilized for parameter optimization, yielding an optimal parameter set: a clamp belt angle of 65°, a theoretical conveying speed of 0. 38 m/s, and a belt speed difference ratio of 1.64. Field test were condueted under the optimized parameters, and the findings indicated that at a forward speed of 0. 16 m/s, the average seed horizontal discharging rate and the average qualified rate of hole distanee achieved by the device were 61. 11% and 78.89%, respectively. The experimental results demonstrated deficits of 4. 17 and 1. 15 percentage points relative to the model-predicted optima. The outcomes of this research can offer valuable insights for the development and design of mechanized orientation seed planting equipment for Allium chinense. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Structural dynamics

Controlled terms: Belt conveyors - Belts - Clamping devices - Cultivation - Dynamic response - Forestry - Model structures - Structural health monitoring

Uncontrolled terms: Allium chinense - Coupled simulation - Dement method — multi-flexible body dynamic - Differential ratio - Flexible bodies - Multi flexible body dynamics - Orientation seed discharging - Planter - Plantings - Speed differential

Classification code: 214.2 Non-mechanical Properties of Materials - 215.2 Testing of Non-mechanical Properties of Materials - 408 Structural Design - 601.2 Machine Components - 602.2 Mechanical Transmissions - 605.2 Small Tools, Unpowered - 692.1 Conveyors - 821.1 Woodlands and Forestry - 821.4 Agricultural Methods - 1201.12 Modeling and Simulation

Numerical data indexing: Percentage 1.10E 01%, Percentage 7.889E 01%, Velocity 1.60E 01m/s, Velocity 3.80E 01m/s

DOI: 10.6041/j.issn.1000-1298.2025.05.023

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

33. Multi-scale Chestnut Detection Method Based on Improved YOLO 11 Model

Accession number: 20252118477605

Title of translation: 基于改进YOLO 11的多尺度板栗果实识别方法

Authors: Li, Mao (1, 2); Xiao, Yangyi (1, 2); Zong, Wangyuan (1, 2)

Author affiliation: (1) College of Engineering, Huazhong Agrieultural Univerdty, Wuhan; 430070, China; (2) Key Laboratory of Agriculture 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 443-454

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the current limitations in detecting chestnut of varying scales under natural eonditions, an innovative multi-scale chestnut detection method was introduced, YOLO 11 — MCS, based on an improved YOLO 11 model. Firstly, a novel multi-scale key feature aggregation (MKFA) module was proposed, which was integrated into the C3k2 module to form the C3k2 — MKFA feature extraction module, effectively capturing features at different scales, enhancing multi-scale feature extraction capabilities. Subsequently, the CGAFPN network was introduced, which incorporated a small object detection layer through a content-guided attention module and increased the contribution proportion of chestnut small object to multi-scale object, overcoming the deficiencies of the original algorithm in multi-scale and small object detection. Finally, a shared convolution separated batch normalization detection head (SCSB) was presented, utilizing shared convolution and separated batch normalization structures to efficiently extract cross-scale features and enhance feature consistency across different scales, effectively improved the Performance of multi-scale object detection. Experimental results demonstrated that the improved model achieved a chestnut detection precision of 88. 2%, a recall rate of 79. 2%, and an average precision of 87.2%, which had improvements of 0. 8, 5.9, and 5.5 percentage points, respectively, compared with the original YOLO 11 network. The model with channel-wise feature distillation achieved an average precision of 84. 7%, with a model size of 6. 0 MB. When deployed on the Jetson Nano using the Infer inference library, the detection speed was 23 ms per image, meeting the requirements for chestnut detection. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Feature Selection

Uncontrolled terms: Chestnut - Detection methods - Feature aggregation - Features extraction - Key feature - Knowledge distillation - Multi-scales - Objects detection - Small object detection - YOLO 11-MCS

Classification code: 1101.2 Machine Learning

Numerical data indexing: Percentage 2.00E 00%, Percentage 7.00E 00%, Percentage 8.72E 01%, Time 2.30E-02s

DOI: 10.6041/j.issn.1000-1298.2025.05.042

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

34. Instance Segmentation Method of Pre-weaning Piglets Based on OD_SeGAN

Accession number: 20252118477623

Title of translation: 基于OD_SeGAN的断奶前仔猪实例分割方法

Authors: Li, Peng (1, 2); Shen, Mingxia (1, 2); Liu, Longshen (1, 2); Chen, Jinxin (2); Xue, Hongxiang (2); Heng, Xi (2); Sun, Yuwen (2)

Author affiliation: (1) College of Artijicial Intelligence, Nanjing Agricultural University, Nanjing; 210031, China; (2) Key Laboratory of Breeding Equipment, Ministry of Agriculture and Rural Affairs, Nanjing; 210031, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 482-491

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the research on smart pig breeding, the pig instance segmentation method is one of the key technologies to realize automatic detection of pigs. However, in actual segmentation seenarios, there is occlusion and adhesion phenomenon, which makes pig segmentation difficult. Aiming at the difficulty of piglet segmentation in the farrowing room, an instance segmentation model OD_SeGAN was proposed based on YOLO v5s and generative adversarial network (GAN). This method extracted the piglet target through the target detection algorithm YOLO v5s, and inputed it into the semantic segmentation algorithm GAN to achieve segmentation, and used dilated convolution to replace the ordinary convolution in GAN to expand the network receptive field; secondly, a squeeze-incentive attention mechanism was used module to enhance the model’s ability to learn the global characteristics of piglets and improve the model’s segmentation accuracy. Experimental results showed that OD_SeGAN’s IoU on the test set was 88. 6%, which was 3.4, 3.3, 4. 1, 9.7, and 8. 1 percentage points higher than YOLO v5s_Seg, Cascade_Mask _RCNN, Mask_RCNN, SOLO, and Yolact, respectively. OD_SeGAN was applied to the piglet litter average weight estimation task, and the Pearson correlation coefficient between the piglet litter average weight and the number of piglet pixels was measured to be 0. 956. The OD_SeGAN proposed had good piglet segmentation Performance in actual production seenarios, and can provide a technical basis for subsequent research such as piglet litter weight estimation. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Semantic Segmentation

Controlled terms: Agricultural implements - Harvesters

Uncontrolled terms: Adversarial networks - Attention mechanisms - Automatic Detection - Average litter weight - Instance segmentation - Key technologies - Piglet - Segmentation methods - Smart pigs - Weights estimation

Classification code: 821.2 Agricultural Machinery and Equipment - 1106.8 Computer Vision

Numerical data indexing: Percentage 6.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.046

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

35. Simulation of Evapotranspiration in Winter Wheat Considering Solar-induced Chlorophyll Fluorescence

Accession number: 20252118477592

Title of translation: 考虑日光诱导叶绿素荧光的冬小麦蒸散量模拟

Authors: Li, Yao (1, 2); Liu, Jiangzhou (1, 2); Liu, Xuanang (1, 2); Zhao, Zhengxin (1, 2); Peng, Xiongbiao (1, 2); Cai, Huanjie (1, 2)

Author affiliation: (1) College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Institute of Water-saving Agriculture in Arid Areas of China, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 534-542

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to investigate the Simulation effect of machine learning model on actual evapotranspiration (ETa) of winter wheat during the reproductive period and the effect of solar-induced Chlorophyll fluorescence (SIF) on the Simulation accuracy of machine learning model in the absence of meteorological data, SIF was combined with meteorological indicators, crop physiological indicators, soil thermal conditions and other factors, and three classical machine learning models, namely the gradient boosting (GB), random forest (RF), and support vector machine (SVM) were constructed, combined with linear regression (LR) model to simulate winter wheat ETa and compared with the evapotranspiration ET_pm calculated by Penman — Monteith (P — M) model. The results showed that although SIF was significantly correlated with ETa, the fitting accuracy of the machine learning model constructed only by using SIF as a feature parameter was low; according to the importance ranking of the feature parameters based on the machine learning model as well as the Simulation accuracy of the model under each scenario, it was known that SIF had an enhancement effect on the accuracy of the machine learning model in simulating ETa. The machine learning model fit better than the P — M model when there were enough feature parameters, and adding feature parameters to the average temperature, SIF, sunshine hours, leaf area index (LAI) and soil moisture content did not improve the Simulation accuracy, so it was recommended to use the feature set composed of the five feature parameters mentioned above to construct a machine learning model to predict ETa. The R of the models were 0. 92, 0. 91 and 0. 91, respectively, among which the GB model had the best fitting effect on the ETa of winter wheat during the whole reproductive period. The research result can provide a reference for the accurate Simulation of local evapotranspiration and the development of rational irrigation System in the absence of meteorological data. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Support vector machines

Controlled terms: Harvesters

Uncontrolled terms: Chlorophyll fluorescence - Eddy covariance systems - Feature parameters - Gradient boosting - Machine learning models - Meteorological data - Penman-Monteith models - Simulation accuracy - Solar-induced chlorophyll fluorescence - Winter wheat

Classification code: 821.2 Agricultural Machinery and Equipment - 1101.2 Machine Learning

DOI: 10.6041/j.issn.1000-1298.2025.05.051

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

36. UAV Monitoring of Cotton Aphid Damage Levels Based on Fusion of Spectral Bands, Texture Features and Vegetation Indices

Accession number: 20252118472015

Title of translation: 基于光谱波段-纹理特征-植被指数融合的棉蚜虫危害等级无人机监测研究

Authors: Liao, Juan (1, 2); Wang, Hui (1, 2); Liang, Yexiong (1, 2); He, Xinying (1, 2); Zeng, Haoqiu (1, 2); He, Songwei (1, 2); Tang, Saiou (1, 2); Luo, Xiwen (1, 3)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou; 510642, China; (3) Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL — AAI), Guangzhou; 510642, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 91-102

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate and nondestmctive deteetion of cotton aphids is crucial for effective pest control and enhancing cotton yield and quality. Aiming to propose a multi-feature fusion method for cotton aphid damage level (CADL) monitoring, spectral feature wavelengths, Vegetation indices, and cotton canopy texture characteristics were integrated to enhance the accuracy of cotton aphid damage level determination. A UAV-mounted hyperspectral imaging System was employed to collect hyperspectral image data of cotton canopy. Pre-processing of the extracted spectral data involved Savitzky — Golay smoothing (SG smoothing) and multiple scattering correction (MSC). Support vector machine (SVM) modeling was applied to the pre-processed speetral data, results revealed that MSC performed better than SG smoothing in pre-processing. Thus the speetral data pre-processed by MSC was used for charaeteristic wavelengths extraction. Charaeteristic wavelengths extraction was conducted by using the competitive adaptive reweighting algorithm (CARS) and the shuffled frog leaping algorithm (SFLA), totally 31 and 37 charaeteristic wavelengths were extracted by CARS and SFLA, respectively. Subsequently, the successive projeetions algorithm (SPA) was utilized for secondary charaeteristic wavelengths extraction. Ultimately, six sensitive wavelengths at wavelengths of 650 nm, 786 nm, 931 nm, 938 nm, 945 nm and 961 nm were extracted. Based on six secondarily extracted charaeteristic wavelengths, nine Vegetation indices and eight texture features were calculated, followed by correlation analysis between these Vegetation indices/texture features and CADL. Four machine learning models (LightGBM, XGBoost, SVM, RF) were developed to evaluate the Classification Performance by using charaeteristic wavelengths alone, Vegetation indices alone, texture features alone, combined charaeteristic wavelengths and Vegetation indices, and integrated charaeteristic wavelengths, Vegetation indices, and texture features. Results indicated that Vegetation indices (RDVI, SAVI, MSAVI, OSAVI) and texture features (MEA, VAR, DIS, HOM) exhibited strong correlations with CADL. The XGBoost model incorporating the tri-feature combination (charaeteristic wavelengths, Vegetation indices, texture features) achieved optimal CADL Classification Performance, yielding an overall aecuraey (OA) of 86. 99% and a Kappa coefficient of 0. 837 1 on the test set. Compared with models by using charaeteristic wavelengths alone, Vegetation indices alone, texture features alone, or the dual-feature combination (charaeteristic wavelengths, Vegetation indices), this integrated approach improved OA by 4.88, 27.64, 21.95, and 2.44 percentage points, respectively. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Damage detection

Controlled terms: Image enhancement - Photointerpretation - Support vector machines

Uncontrolled terms: Aerial remote sensing - Cotton aphid - Cotton aphid damage level - Damage level - HyperSpectral - Multi-feature fusion - Multiple-scattering - Scattering corrections - Texture features - Vegetation index

Classification code: 742.1 Photography - 913.3.1 Inspection - 1101.2 Machine Learning - 1106.3.1 Image Processing

Numerical data indexing: Percentage 9.90E 01%, Size 6.50E-07m, Size 7.86E-07m, Size 9.31E-07m, Size 9.38E-07m, Size 9.45E-07m, Size 9.61E-07m

DOI: 10.6041/j.issn.1000-1298.2025.05.009

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

37. Automatic Measurement Method of Body Size of Group-raised Pigs Based on Improved YOLO v5-pose

Accession number: 20252118477638

Title of translation: 基于改进YOLO v5-pose的群养生猪体尺自动测量方法

Authors: Liu, Gang (1, 2); Zeng, Xueting (1, 2); Liu, Xiaowen (1, 2); Li, Tao (3); Ding, Xiangdong (4, 5); Mi, Yang (1, 2)

Author affiliation: (1) Key Laboratory of Smart Agriculture Systems Integration, Ministry of Education, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Information Acquisitum Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing; 100083, China; (3) Henan Fengyuan Hepu Agricultural and Animal Husbandry Co., Ltd., Xinyang; 464000, China; (4) College of Animal Science and Technology, China Agricultural University, Beijing; 100193, China; (5) Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing; 100193, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 455-465

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problem that it is diffioult to extract body measurement points efficiently and accurately in the automatic measurement of body size of group-raised pigs, an automatic measurement method of body size of group-raised pigs based on improved YOLO v5 — pose was proposed. Firstly, the convolutional block attention module (CBAM) was integrated into the YOLO v5 —pose baekbone network to better capture the relevant features of the measurement points. Then the C3 traditional module of the Neck layer was replaced with the C3Ghost lightweight module to reduce the number of model parameters and memory usage. Finally, the dynamic head (DyHead) target detection head was introduced in the Head layer to enhance the model’s ability to represent the position of the measurement points. The results showed that the average accuracy of the improved model was 92. 6%, the number of parameters was 6. 890 xl0\ and the memory usage was 14. 1 MB. Compared with the original YOLO v5 - pose model, the average accuracy was increased by 2. 1 pereentage points, and the number of parameters and memory usage were decreased by 2.380 X 10 and 0.4 MB, respectively. Compared with the current classic models YOLO v7 — pose, YOLO v8 — pose, real-time multi-person pose estimation based on mmpose (RTMPose) and CenterNet, this model had better recall rate and average precision and was more lightweight. Experiments were conducted on a dataset of 2 400 group-raised pigs images. The results showed that the average absolute errors of the body length, body width, hip width, body height and hip height measured by this method were 4. 61 cm, 5. 87 cm, 6. 03 cm, 0. 49 cm and 0. 46 cm, respectively, and the average relative errors were 2. 69%, 11. 53%, 12. 29%, 0. 90% and 0. 76%, respectively. In summary, the method improved the detection accuracy of body size measurement points, reduced the complexity of the model, and achieved more accurate body size measurement results, providing an effective technical means for the automatic measurement of body size of pigs in group-raising environments. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Uncontrolled terms: Body size measurement - Body sizes - Coordinate transformations - Group-raised pig - Improved YOLO v5 —pose - Key point detection - Keypoints - Measurement points - Point detection - Size measurements

Numerical data indexing: Size 3.00E-02m, Size 4.60E-01m, Size 4.90E-01m, Size 6.10E-01m, Size 8.70E-01m, Percentage 2.90E 01%, Percentage 5.30E 01%, Percentage 6.00E 00%, Percentage 6.90E 01%, Percentage 7.60E 01%, Percentage 9.00E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.043

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

38. Pitaya Fruit Target Detection and Localization Method Based on Improved MBS YOLO v8

Accession number: 20252118477619

Title of translation: 基于改进MBS-YOLO v8的火龙果目标检测

Authors: Liu, Jinyi (1); Yan, Fushan (1); Dong, He (1); Fu, Lirong (1); Fu, Wei (1); Chen, Yu (2)

Author affiliation: (1) School of Mechanical and Electrical Engineering, Hainan University, Haikou; 570228, China; (2) College of Mechanical and Electronic 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 425-432

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issue of overlapping occlusion caused by the varying sizes and large quantities of dragon fruit, a multi-scale weighted feature fusion network (MBS — YOLO v8) was proposed based on the YOLO v8 model. Firstly, the squeeze-and-excitation attention (SEAttention) mechanism was incorporated into the feature extraction module to enhance the network’s ability to capture critical details, thereby addressing the ehallenge of small object detection. Secondly, a multi-scale weighted fusion network (MWConv) was introdueed to generate feature maps with varying receptive fields, improving the capture of global features within images. Finally, experimental results demonstrated that MBS — YOLO v8 achieved an accuracy of 92. 5%, a recall rate of 90. 1%, and a mean average precision (mAP50) of 94. 7%. Compared with the YOLO v8n algorithm, MBS — YOLO v8 showed improvements of 2. 1 percentage points, 5.9 percentage points, and 2 percentage points in accuracy, recall, and mAP50, respectively. The proposed MBS — YOLO v8 model exhibited high robustness, effectively integrating global feature Information with low-dimensional local features to enhance the model’s understanding of image content. This approach effectively addressed challenges related to overlapping oeelusion and small object detection, providing an improved methodology for detecting dragon fruit and other similar targets. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Feature extraction

Controlled terms: Image enhancement - Object detection - Object recognition - Photointerpretation

Uncontrolled terms: Features fusions - Global feature - Multi-scale weighted feature fusion network - Multi-scales - Objects detection - Percentage points - Pitaya fruit - Small object detection - Small objects - Weighted features

Classification code: 742.1 Photography - 1101.2 Machine Learning - 1106.3.1 Image Processing - 1106.8 Computer Vision

Numerical data indexing: Percentage 1.00E00%, Percentage 5.00E 00%, Percentage 7.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.040

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

39. Traceability Model of Machine-picked Cotton Quality Based on Blockchain

Accession number: 20252118471992

Title of translation: 基于区块链的机采棉质量溯源模型研究

Authors: Liu, Kang (1, 2); Zhang, Mengyun (1, 2); Chang, Jinqiang (1, 2); Xu, Jiankang (1, 3); Song, Yuhan (1, 3); Hu, Rong (1, 2)

Author affiliation: (1) College of Mechanieal and Electrieal Engineering, Shihezi University, Shihezi; 832003, China; (2) Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi; 832003, China; (3) Innovation Center for Digital Equipment and Technology for Smart Farms, Xinjiang Produktion and Construction Corps, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 141-149

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: With the widespread adoption of mechanieal cotton harvesting in Xinjiang, challenges such as data silos, delayed information sharing, and inadequate whole-process monitoring have hindered precise and transparent quality traceability from seed cotton to lint cotton. To address this, a blockchain-based quality traceability model for machine-harvested cotton was proposed by leveraging the decentralized and tamper-resistant features of blockchain technology and the real-time data acquisition capabilities of IoT devices. A logistic regression-based off-chain/on-chain collaborative data query optimization was introduced to achieve intelligent pre-caching of high-frequency data. Additionally, an access control model integrating reinforcement learning and elliptic curve cryptography was designed to enhance data security and privaey protection. The quality traceability System was developed on the ChainMaker open-source blockchain platform. Performance tests demonstrated that the System reduced query latency from 72. 37 ms to 60. 14 ms in regulär scenarios and further decreased it to 32. 75 ms in high-frequency scenarios, with optimization efficiency improving as data volume increased, meeting real-time user query demands. In addition, through plaintext sensitivity and key sensitivity tests, confirming average ciphertext change rates of 87. 78% and 82. 68%, respectively. These results ensured the privaey and security of data during cross-institutional collaboration. The model established a closed-loop architecture of “IoT data collection — blockchain notarization — smart contract verification — multi-level access control” fulfilling enterprises’ requirements for privacy data permission management and secure sharing while enhancing Information retrieval efficiency. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Access control models

Controlled terms: Authentication - Authorization - Information management - Medium access control - Network security - Open access - Reinforcement learning - Sensitive data

Uncontrolled terms: Block-chain - Chainmaker - Cotton harvesting - Cotton qualities - Machine-learning - Machine-picked cotton - Quality traceabilitys - Quality traeeability model - Traceability model - Xinjiang

Classification code: 903 Information Science - 903.3 Information Retrieval and Use - 1101.2 Machine Learning - 1103.3 Data Communication, Equipment and Techniques - 1106 Computer Software, Data Handling and Applications - 1108.1 Cybersecurity - 1108.2.1 Encryption

Numerical data indexing: Percentage 6.80E 01%, Percentage 7.80E 01%, Time 1.40E-02s, Time 3.70E-02s to 6.00E-02s, Time 7.50E-02s

DOI: 10.6041/j.issn.1000-1298.2025.05.014

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

40. Development and Validation of Traction Performance for Mountain Tracked Chassis on Slopes

Accession number: 20252118477968

Title of translation: 山地履带式底盘坡地牵引性能预测模型构建与试验

Authors: Liu, Qi (1, 2); Ji, Yuxuan (1, 2); Yang, Fuzeng (1, 2); Zhang, Longhai (1, 2); Du, Zixing (1, 2); Liu, Zhijie (1, 2); Zhu, Xiaoyan (3)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Scientific Ohserving and Experimental Station of Agricultural Equipment for the Northern China, Ministry of Agriculture and Rural Affairs, Shaanxi, Yangling; 712100, China; (3) Weichai Lovol Intelligent Agricultural Technology Co., Ltd., Weifang; 261000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 597-607

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to accurately and effectively prediet the traction Performance of tracked undercarriages under the complex operating environment in hilly and mountainous areas, a prediction model for the traction Performance of tracked undercarriages was established and experimentally verified based on the law of grounding pressure distribution. Firstly, a mathematical model of ground pressure distribution with multi-peak non-linear distribution was proposed by considering the slope angle and chassis parameters, and the ground pressure test was carried out at different slopes and postures, and the results showed that the average error of its prediction was about 4. 7%, the model can better prediet the distribution of grounding pressure of crawler chassis in slope environment. Secondly, based on the grounding pressure model and the track - ground interaction law, by considering the soil characteristics, slope and the change of the position of the center of gravity for the attitude adjustment of the crawler chassis, the traction-slip rate prediction model of the crawler chassis composed of the driving force characteristics of the evenly distributed vertical load and the non-uniform load control part was further construeted. Finally, the traction Performance tests of contour driving and longitudinal climbing conditions were carried out based on the three types of crawler chassis to verify the prediction model, and the results showed that the average prediction error of the model was 3.6%, 5.4% and 6.3%, respectively, and the overall prediction error was small, which could provide theoretical basis and data support for the design and development of the applicability of the tracked chassis in hilly and mountainous areas and the optimization of maneuverability. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Uncontrolled terms: Ground pressure - Hilly and mountainous areas - Hilly mountainous terrain - Model prediction - Mountainous terrain - Multi-peaks - Operating environment - Prediction modelling - Tracked chassi - Traction performance

Numerical data indexing: Percentage 3.60E 00%, Percentage 5.40E 00%, Percentage 6.30E 00%, Percentage 7.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.058

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

41. Lightweight Cotton Boll Detection Model and Yield Prediction Method Based on Improved YOLO v8

Accession number: 20252118471988

Title of translation: 基于改进YOLO v8的轻量化棉铃识别模型与产量预测方法研究

Authors: Liu, Xiang (1, 2); Xiang, Ruoxue (1, 2); Ban, Chenglong (1, 2); Tian, Min (1, 2); Tan, Mingtian (1, 2); Huang, Kaiwen (1, 2)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 130-140

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Cotton boll count is a critical phenotypio trait for estimating eotton yield and plays a vital role in precision agrieultural management. However, aecurately detecting eotton bolls in densely planted fields remained challenging due to complex baekgrounds, oeelusion, and varying illumination conditions. Highresolution UAV imagery was employed to capture cotton field seenes in a densely planted area of Xinjiang. A comprehensive dataset was developed through image segmentation and augmentation teehniques, ensuring diverse representations of field conditions. To address the trade-off between detection accuracy and computational efficiency, an improved lightweight detection model IML — YOLO was proposed. The model integrated a novel GRGCE module that combined efficient ghost convolution with a RepGhostCSPELAN structure for feature extraction, a CAHSFPN feature fusion mechanism to enhance multi-scale representation, and a Focaler — MPDIoU loss function to refine localization accuracy. Extensive experiments demonstrated that IML — YOLO reduced computational complexity by 32. 1%, decreased model size by 47. 5%, and lowered parameter count by 50% oompared with that of the baseline YOLO v8n, while boosting mean average precision by 10. 1 percentage points. Furthermore, when applied to cotton yield prediction, the model achieved an average relative error of only 7.22%. These findings indicated that the proposed IML — YOLO model and yield prediction methodology can offer an effective Solution for real-time cotton boll detection and significantly contribute to the advancement of intelligent cotton management. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Cotton

Controlled terms: Agricultural implements - Combines - Semantic Segmentation

Uncontrolled terms: Cotton boll detection - Detection models - Features fusions - High resolution - Illumination conditions - Model prediction - Prediction methods - UAV remote sensing - Yield prediction - YOLO v8

Classification code: 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products - 1106.8 Computer Vision

Numerical data indexing: Percentage 1.00E00%, Percentage 5.00E 00%, Percentage 5.00E 01%, Percentage 7.22E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.013

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

42. Surface Residual Film Recognition Method Based on Vehicle-mounted Imaging and Deep Convolutional Neural Networks

Accession number: 20252118472032

Title of translation: 基于车载成像与深度卷积神经网络的地表残膜识别方法

Authors: Lü, Jidong (1, 2); Zhai, Zhiqiang (1, 3); Meng, Qingjian (1, 2); Miao, Lupeng (1, 2); Chen, Yue (1, 2); Zhang, Ruoyu (1, 2)

Author affiliation: (1) College of Mechanieal and Electrieal Engineering, Shihezi University, Shihezi; 832003, China; (2) Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi; 832003, China; (3) Technology Innovation Center of Smart Farm Digital Equipment, Xinjiang Produktion and Construction Corps, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 26-37 and 70

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges in aecurately assessing residual film coverage due to interference from multiple similar non-target scenarios, complex background textures in target scene images, and the small size, high fragmentation, and irregulär eontours of residual films during the operational process of residual film recovery machinery, a residual film recognition method was proposed based on vehicle-mounted imaging and deep convolutional neural networks. A multi-feature-enhanced SE — DenseNet — DC Classification model was developed by integrating Channel attention mechanisms before and after the nonlinear combination functions in each dense block of the DenseNetl21 architecture, the model enhanced the weighting of effective feature Channels. Additionally, the first-layer convolution of the original model was replaced with multi-scale cascaded dilated convolutions to expand the receptive field while preserving sensitivity to fine details, enabling effective extraction of target scene images. Furthermore, a CDC — TransUnet segmentation model was constructed with enhanced detail Information and multi-scale feature fusion. In the encoder of the TransUnet framework, CBAM modules were introduced to capture finer and more precise global features. DAB modules were embedded in the skip connections to fuse multi-scale semantie Information and bridge the semantic gap between encoder and decoder features. CCAF modules were then incorporated into the decoder to mitigate detail loss during upsampling, achieving precise segmentation of residual films against complex backgrounds in target scenes. Experimental results demonstrated that the SE — DenseNet — DC Classification model achieved Classification accuracy, precision, recall, and Fl score of 96. 26%, 91.54%, 94.49%, and 92. 83%, respectively, for target scene image Classification. The CDC — TransUnet segmentation model achieved an average intersection over union (MIOU) of 77. 17% for surface residual film segmentation. The coefficient of determination (R) between the predicted and manually annotated film coverage was 0.92, with root mean Square error (RMSE) of 0.23%, and average relative error of 2.95%. The average evaluation time was 0. 54 s per image. This method demonstrated high accuracy and rapid processing capabilities for real-time monitoring and evaluation of residual film coverage post-recovery, providing robust technical support for quality assessment in residual film recovery Operations. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Semantic Segmentation

Controlled terms: Image coding - Image enhancement - Network coding

Uncontrolled terms: Complex background - Convolutional neural network - Cotton fields - Film coverage - Identification - Recognition methods - Residual film recycling - Residual films - Target scenes - Vehicle-mounted imaging

Classification code: 716.1 Information Theory and Signal Processing - 1106.3.1 Image Processing - 1106.8 Computer Vision

Numerical data indexing: Percentage 2.95E 00%, Percentage 8.30E 01%, Percentage 9.154E 01%, Percentage 9.449E 01%, Time 5.40E 01s, Percentage 1.70E 01%, Percentage 2.30E-01%, Percentage 2.60E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.003

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

43. Recognition Method of Cotton Field Surface Residual Film Based on Improved YOLO 11

Accession number: 20252118472020

Title of translation: 基于改进YOLO 11模型的棉田地表残膜识别方法研究

Authors: Meng, Qingjian (1, 2); Zhai, Zhiqiang (1, 2); Zhang, Lianpu (1, 2); Lü, Jidong (1, 2); Wang, Huting (2); Zhang, Ruoyu (1, 2)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 17-25 and 48

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the issue of estimating the recovery rate of residual film in cotton fields by current residual film recovery machines, a lightweight residual film recognition method named DCA — YOLO 11 was proposed, which enabled rapid and accurate identification of residual film on cotton field surfaces in natural environments. Taking the residual film on cotton field surfaces after the Operation of the 4JMLE —210 residual film recovery machine as the research object, totally 900 images of residual film were collected at different time periods. Through preprocessing steps such as perspective transformation, image cropping, data cleaning, and data augmentation, a dataset of 5 215 residual film sample images was constructed, which was divided into training and test sets at a 4: 1 ratio. To enhance the model’s Performance, a depthwise convolution (DWConv) module was added to the backbone network of YOLO 11 to replace a Standard convolution (Conv) module, thereby reducing computational complexity and the number of parameters. Additionally, a CBAM attention mechanism module was incorporated at the end of the detection Output to improve the model’s perception eapability and reduce interference from edges and backgrounds. Furthermore, the ADown module was used to replace the Conv module in the backbone network, enabling downsampling between different layers of the residual film feature maps, reducing the spatial dimensions of the feature maps while retaining key Information to improve the accuracy of residual film target detection. Experimental results demonstrated that the DCA — YOLO 11 model achieved a precision (P) of81.9%, a recall (R) of 80. 9%, and a mean average precision (mAP) of 86. 7% (at an IoU threshold of 0. 5) in complex natural environments. The model has about 2. 20 million Parameters, and an FPS of 80 f/s. Comparative experiments with other models showed that DCA — YOLO 11 outperformed YOLO vlO, YOLO v9 and YOLO v8 in precision by 2.9 percentage points, 2. 3 percentage points, 3. 8 percentage points. In terms of recall, it was improved by 2. 0 percentage points, 1.0 percentage points, and 1.8 percentage points compared with that of YOLO vlO, YOLO v9, and YOLO v8, respectively. While its processing speed was slightly lower than than that of YOLO vlO, and it surpassed YOLO v9 and YOLO v8 by 12.7% and 14.2%. DCA - YOLO 11 achieved the smallest model size and the fewest parameters while maintaining high accuracy, demonstrating its lightweight design and superiority. Through generalization test, the model’s detection results on the Validation dataset showed an R of 0. 72, a mean absolute error (MAE) of 4. 92 pcs and a root mean Square error (RMSE) of 2. 72 pcs, indicating good generalization. The research result can provide a theoretical foundation and data Support for the precise and efficient collection of residual film by recovery machinery in complex environments, as well as for the visual estimation of the recovery rate of residual film recovery machines. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Image enhancement

Controlled terms: Image coding

Uncontrolled terms: Cotton fields - Model lightweight - Natural environments - Percentage points - Recognition methods - Recovery rate - Residual film identification - Residual films - Targets detection - YOLO 11 model

Classification code: 1106.3.1 Image Processing

Numerical data indexing: Percentage 1.27E 01%, Percentage 1.42E 01%, Percentage 7.00E 00%, Percentage 9.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.002

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

44. Standard System for Smart Cotton Production Farms

Accession number: 20252118472003

Title of translation: 棉花生产智慧农场标准体系研究

Authors: Pan, Hao (1, 2); Zhang, Ruoyu (1, 2); Cai, Fengjie (1, 2); Hu, Huibing (1, 2); Li, Yulin (1, 2)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 38-48

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The construction of a Standard System is a fundamental project for achieving the standardization of smart farms. In response to the key issues existing in the current construction of smart farms for cotton production, such as fragmented Standard Systems, low levels of standardization, and incomplete data sharing mechanisms, based on a systematic review of the current Status and standardization needs of smart farms for cotton production, the core principles and paths for the construction of the Standard System were established. Through integrating modified Checkland methodology and Hall’s three-dimensional structure model, a novel three-dimensional architecture encompassing hierarchical, procedural, and professional dimensions was proposed. The developed framework comprised five standardized Clusters; fundamental and general Standards, data Standards, product Standards, methodological Standards, and management/ Service Standards, achieving vertical Integration across Standard levels and horizontal coverage of operational processes. To validate System efficacy, a fuzzy analytic hierarchy process (FAHP) comprehensive evaluation model incorporating four primary and seven secondary indicators was established, with evaluation results demonstrating good applicability (grade H) and significant implementation value. This research provided theoretical foundations for Standard System development in China’s smart cotton production. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Calibration

Uncontrolled terms: Cotton production - Current construction - Data Sharing - Incomplete data - Key Issues - Mechanism-based - Sharing mechanism - Smart farm - Standard system - Systematic Review

DOI: 10.6041/j.issn.1000-1298.2025.05.004

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

45. Moisture Regain Detection of Seed Cotton Using Information Fusion Based on Stacking Ensemble

Accession number: 20252118471989

Title of translation: 基于Stacking集成的籽棉回潮率信息融合检测方法研究

Authors: Qian, Yifu (1, 2); Huang, Jie (1, 2); Fang, Liang (1, 2); Duan, Hongwei (1, 3); Zhang, Mengyun (1, 3)

Author affiliation: (1) College of Mechanieal and Electrieal Engineering, Shihezi University, Shihezi; 832003, China; (2) Key Laboratory of Northwest Agrieultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi; 832003, China; (3) Technology Innovation Center of Smart Farm Digital Equipment, Xinjiang Produktion and Construction Corps, 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 159-166

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Moisture regain is a critieal indicator of eotton quality, playing an essential role in cotton trading and processing. With the mechanization rate of cotton harvesting in Xinjiang exceeding 85%, accurately detecting moisture regain in machine-harvested seed cotton has become indispensable for transaction settlement. However, existing resistive-based methods for detecting seed cotton moisture regain exhibit low accuracy and poor robustness. An Information fusion detection method leveraging resistive technology to achieve precise moisture regain measurement was proposed. Seed cotton samples, specifically the Xinluzao 80 variety, were collected from a cotton processing enterprise in Changji City, Xinjiang. From the same batch of seed cotton, totally 200 g per group was randomly selected. Under varying environmental temperature and humidity conditions, totally 50 g from each group was dried by using constant-temperature ovens to determine the true moisture regain. The remaining 150 g was tested in a constant temperature and humidity Chamber by using a resistive sensing technique with pressure compensation. In total, 517 sets of resistance values and corresponding moisture regain data were obtained. The relationship between seed cotton moisture regain, environmental conditions, and cotton density was analyzed, determining the influence of density on resistive measurements. Using environmental temperature, humidity, seed cotton resistance, and density as feature variables, five regression models were established; multiple linear regression (MLR), support vector regression (SVR), random forest (RF), backpropagation neural network (BPNN), and K-nearest neighbors (KNN). Additionally, a staeking ensemble model was constructed to integrate data-level and decision-level information fusion. The dataset was split into training, Validation, and test sets at a ratio of 6^2:2, and hyperparameters were optimized by using grid search. RF, BPNN, and KNN served as base learners, while MLR was employed as the meta-learner in the staeking ensemble model. Comparative analysis revealed that the staeking ensemble model outperformed the individual models, achieving a coeffieient of determination (R) of 0. 994, a root mean Square error (RMSE) of 0. 151%, and a mean absolute error (MAE) of 0. 104% on the test set. These results validated the reliability of the proposed information fusion detection method. The staeking ensemble model demonstrated superior performanee and stability aeross Validation and test sets compared with single models. This approaeh was well-suited for moisture regain deteetion during cotton harvesting, baling, and trading, providing robust data support for trade settlement and process optimization. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Image fusion

Controlled terms: Logistic regression - Multiple linear regression - Polynomial regression - Sensor data fusion

Uncontrolled terms: %moisture - Ensemble models - Prediction modelling - Regression prediction model - Regression predictions - Resistance detection - Seed cotton - Seed cotton moisture regain - Staeking ensemble model - Test sets

Classification code: 1106.2 Data Handling and Data Processing - 1106.3.1 Image Processing - 1202 Statistical Methods - 1202.2 Mathematical Statistics

Numerical data indexing: Mass 1.50E-01kg, Mass 2.00E-01kg, Mass 5.00E-02kg, Percentage 1.04E 02%, Percentage 1.51E 02%, Percentage 8.50E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.016

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

46. Feature Matching Algorithm Based on Improved Binocular ORB SLAM3

Accession number: 20252118477981

Title of translation: 基于改进双目ORB-SLAM3的特征匹配算法

Authors: San, Hongjun (1, 2); Feng, Jinxiang (1); Chen, Jiupeng (1, 2); Peng, Zhen (1); Zhao, Longyun (1)

Author affiliation: (1) Faculty of Mechanical and Electrical Engineering, Kunming University of Scienee and Technology, Kunming; 650500, China; (2) Yunnan Provincial Key Laboratory of Advanced Equipment Intelligent Manufacturing 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 625-634

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problem that the traditional ORB algorithm fails to meet the high-precision localization requirements due to the high mis-matching rate in the binocular feature matching stage, a feature matching algorithm based on the improved binocular ORB-SLAM3 is proposed. The nearest neighbor matching algorithm (FLANN) is introduced in the feature point matching stage, and more accurate matching pairs are filtered out by setting the ratio threshold, and the adaptive weighted SAD — Census algorithm is introduced in the binocular ORB — SLAM3 three-dimensional matching, and the geometric distances between the cases are taken into account to recalculate the SAD values and merge them with the Census algorithm to improve the stability and accuracy of feature matching, while the adaptive weighted SAD — Census algorithm is introduced. At the same time, the adaptive SAD window sliding ränge is added to further expand the search distance, so as to filter out the correct matches to improve the accuracy of the System. Experiments are carried out in the EuRoC dataset and real indoor scenes, and the results show that compared with the pre-improved ORB — SLAM3 algorithm, the localization accuracy of the improved algorithm is improved by 23. 32% in the dataset, and nearly 50% in the real environment, thus verifying the feasibility and effectiveness of the improved algorithm. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Feature extraction

Controlled terms: Adaptive algorithms

Uncontrolled terms: Adaptive weighted SAD - Census algorithms - Feature matching algorithms - Features matching - Improved binocular ORB - Matching algorithm - Matchings - Near neighbor matching algorithm - Nearest neighbor matching - SLAM3

Classification code: 1101.2 Machine Learning

Numerical data indexing: Percentage 3.20E 01%, Percentage 5.00E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.061

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

47. Development Status and Prospect of Research on Key Technologies of Cotton Pickers

Accession number: 20252118472014

Title of translation: 采棉机关键技术研究现状与展望

Authors: Shi, Maolin (1, 2); Gao, Xiaoya (1); Zhang, Weidong (1); Ran, Kangli (1); Zhong, Liangyi (1); Xu, Lizhang (2, 3)

Author affiliation: (1) School of Agricultural Engineering, Jiangsu University, Zhenjiang; 212013, China; (2) Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment of Jiangsu University, Zhenjiang; 212013, China; (3) Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment, Zhenjiang; 212013, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 167-183

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Machine-picked cotton has been the main cotton planting mode in China, especially in the main cotton producing areas. Cotton pickers are key equipment for harvesting machine-picked cotton and are also typical representatives of high-end agricultural machinery. Starting from the Operation process and principle of cotton pickers, the literature on unginned cotton picking, unginned cotton collection and transportation, unginned cotton compression and packaging, chassis driving and Walking, and intelligent control of the whole machine were introduced, and the topics, difficulties, problems, and shortcomings of current research were analyzed. After the development of government guidance and free market competition, the research, development, and manufacturing Systems of cotton pickers have been formed in China, based on the introduction, digestion and absorption of foreign advanced technologies. However, there were still some basic scientific problems in cotton pickers, such as the blocking mechanism of the picking head, the dynamic evolution of the pneumatic conveying flow field, and the chassis load spectrum, which have not been solved. The structural design schemes such as the baling mechanism were not innovative and original enough and were still subject to foreign patents. The Operation Status monitoring and intelligent Operation levels were high, but there was a lack of high-precision special Sensors such as unginned cotton flow, cotton bale density/humidity. In view of the above problems and shortcomings, the future research direction was analyzed and prospected from the aspects of unginned cotton picking mechanism, high-fidelity Simulation analysis of pneumatie conveying, dynamic analysis and optimization of Walking chassis, innovative design of baling mechanism, and intelligent control, which can provide reference for the optimization and design, production and manufaeturing, and the control of cotton pickers. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 102

Main heading: Harvesters

Controlled terms: Agricultural implements - Combines - Cotton

Uncontrolled terms: Cotton pickers - Cotton picking - Development prospects - Development status - Key equipment - Key technologies - Plantings - Producing areas - Status and prospect - Unginned cottons

Classification code: 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products

DOI: 10.6041/j.issn.1000-1298.2025.05.017

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

48. Research on Method of Encrypted Sharing of Privacy Data for Tracing Biological Risk Factors in Oyster Supply Chain

Accession number: 20252118477976

Title of translation: 牡蛎供应链生物风险因子溯源隐私数据加密共享方法研究

Authors: Sun, Chuanheng (1, 2); Zhang, Jun (1, 2); Luo, Na (2, 3); Xu, Daming (2, 3); Chen, Feng (2, 3); Xing, Bin (2, 3)

Author affiliation: (1) College of Information Technology, Shanghai Ocean University, Shanghai; 201306, China; (2) National Engineering Research Center of Agricultural Product Quality and Safety Traceability Technology and Application, Beijing; 100097, China; (3) National Engincering 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 577-588

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Oyster agricultural products may carry transmissible viruses, bacteria, and other biological risk factors during their market circulation, which has caused frequent biosecurity incidents in many countries and seriously threatened people’s health. The privacy characteristics of biological risk detection data are significant, and the improper disclosure of biological risk detection Information can pose a threat to public health security. A privacy-preserving data encryption and sharing method for oyster supply chain biological risk factors was proposed based on blockchain technology. By encrypting and storing biological risk factor detection data from various links in the supply chain on the blockchain, data security and sharing were ensured, and the source of the risk can be traced back. The method used attribute-based searchable encryption algorithms to perform access control and privacy protection on biological risk factor detection data, and an encrypted inverted index for query efficiency optimization was constructed. By combining searchable encryption algorithms and inverted indices, rapid location of relevant risk batch goods’ detailed data was achieved. The experimental test results showed that the average encryption time of biological risk detection data keyword was 31 ms, the average trapdoor generation time was 32 ms, and the average matching time of searchable ciphertext and trapdoor was 16 ms. The average time for regulators to query risk oyster agricultural product-related data through encrypted inverted index was 385 ms, and a prototype System was built on the Ethereum blockchain platform to realize the functions of biological risk factor encrypted privacy data storage and traceability query. The results showed that the method can meet the demand of privacy data sharing scenarios in oyster supply chain and provide technical support for oyster biological risk regulation. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Health risks

Controlled terms: Agricultural robots - Anonymity - Authentication - Authorization - Fruits - Medium access control - Privacy by design - Public risks - Risk analysis - Risk assessment - Sensitive data

Uncontrolled terms: Biological risk factor - Biological risks - Block-chain - Data Sharing - Encryption algorithms - Inverted indices - Risk detections - Risk factors - Searchable encryptions - Traceability

Classification code: 102.1.2.1 Health Care - 731.6 Robot Applications - 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products - 901.3 Engineering Research - 902.3 Legal Aspects - 914.1 Accidents and Accident Prevention - 1103.3 Data Communication, Equipment and Techniques - 1106 Computer Software, Data Handling and Applications - 1108 Security and Privacy - 1108.1 Cybersecurity - 1108.2.1 Encryption - 1202 Statistical Methods

Numerical data indexing: Time 1.60E-02s, Time 3.10E-02s, Time 3.20E-02s, Time 3.85E-01s

DOI: 10.6041/j.issn.1000-1298.2025.05.056

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

49. Sheep Multi-object Tracking Method Integrating Depth Information and Motion Trends

Accession number: 20252118477609

Title of translation: 融合深度信息与运动趋势的羊只多目标跟踪方法

Authors: Wang, Meili (1); Yang, Ende (1)

Author affiliation: (1) College of Information 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 475-481 and 491

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In recent years, the application of Information technology in sheep farming has become inereasingly sophisticated, necessitating more accurate individual identification and behavior monitoring. This, in turn, has placed higher demands on the accuracy of multiple object traeking (MOT) algorithms, which formed the foundation of these applications. However, existing MOT algorithms often underperformed in scenarios involving object occlusion and dynamic environments. Two novel traeking cues, depth modulated IoU (DIoU) and tracklet direction modeling (TDM), was proposed, aiming at enhaneing the precision and robustness of multiple object tracking by supplementing the intersection over Union (IoU) cue. DIoU improved the traditional IoU ealeulation by incorporating depth Information of the objects. TDM focused on the movement trends of targets, predicting their future directions based on their historical movement patterns. The DIoU and TDM strategies were integrated into the BoT — SORT algorithm, resulting in an improved multiple object tracking algorithm. Evaluations on two datasets showed that the enhaneed algorithm increased the multiple object tracking accuracy (MOTA) by 1.6 percentage points and 1.7 percentage points and the identification Fl score (IDF1) by 1. 9 percentage points and 1. 0 percentage points, respectively, compared with baseline methods. These results indicated that the improved algorithm significantly enhaneed tracking continuity and accuracy in complex scenarios. This research provided insights and methods for multiple object tracking technology, holding significant implications for practical applications. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Object detection

Controlled terms: Agricultural implements - Agricultural robots - Bot (Internet) - Combines - Harvesters - Milking machines - Wool

Uncontrolled terms: BoT —SORT - Data association - Multi-object track - Multiobject - Multiple object tracking - Object track - Objects detection - Percentage points - Recognition - Sheep

Classification code: 731.6 Robot Applications - 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products - 1106.5 Computer Applications - 1106.8 Computer Vision

DOI: 10.6041/j.issn.1000-1298.2025.05.045

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

50. Design and Experiment of Deep Burial Machine on Sodic Saline-alkali Soil

Accession number: 20252118472027

Title of translation: 苏打盐碱土秸秆深埋机设计与试验

Authors: Wang, Yuxing (1, 2); Wang, Xiaoyan (1, 2); Li, Hongwen (1, 2); Wang, Qingjie (1, 2); Gao, Shijie (1, 2); Liu, Di (1, 2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Scientific Observing and Experiment Station of Arable Land Conservation (North Hebet), 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 202-212

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the low permeability of sodic saline soils and the limited adaptability of eonventional reclamation equipment, a specialized maehine was designed for saline-alkali soil improvement based on deep straw incorporation. Soil column experiments revealed that filling the 20 ~ 40 cm soil layer with straw increased permeability by 59. 52%. A stepped drag-reduction optimization of the trenching device was conducted by using the discrete dement method (DEM) and central composite design (CCD). The results showed that at subsoiler shovel working depth of 38. 4 cm, penetration angle of 24. 7°, and blade angle of 60. 1°, the trenching shovel resistance reached 9 752. 5 N, while the total System resistance was 12 401. 9 N, representing decreases of 27. 7% and 0. 4% compared with that of the control, respectively. Field Validation of the optimized parameter combination revealed that the total System resistance reached 14 500. 5 N, a 2. 6% increase compared with that of the control, whereas the trenching shovel resistance dropped by 16. 5% to 11 801. 8 N. The average trenching depth was 39. 1 cm (coefficient of Variation 5. 06%), and the average trenching width was 9. 4 cm (coefficient of Variation 5. 16%), demonstrating a high degree of operational consistency. The straw burial qualification rate was 80%, indicating that the equipment was capable of effectively accomplishing the deep burial task. Through structural innovation and parameter optimization, the research can effectively address the high-resistance, low-efficiency issues inherent to trenching in sodic saline soils, providing a reliable technical and equipment Solution for saline-alkali land improvement. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Trenching

Controlled terms: Shovels

Uncontrolled terms: % reductions - Deep burial machine - Deep burials - Discrete dement method - Field experiment - Saline soil - Saline-alkali soils - Sodic saline-alkali soil - Stepped drag-reduction - System resistance

Classification code: 605.2 Small Tools, Unpowered - 610.1 Pipe, Piping, and Pipelines

Numerical data indexing: Force 5.00E 00N, Force 8.00E 00N, Force 9.00E 00N, Percentage 1.60E 01%, Percentage 4.00E 00%, Percentage 5.00E 00% to 1.10E 01%, Percentage 5.20E 01%, Percentage 6.00E 00%, Percentage 7.00E 00%, Percentage 8.00E 01%, Size 1.00E-02m, Size 2.00E-01m to 4.00E-01m, Size 4.00E-02m

DOI: 10.6041/j.issn.1000-1298.2025.05.019

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

51. Crop Water Footprint Efficiency and Its Driving Forces in Rice Wheat Rotation System

Accession number: 20252118477994

Title of translation: 稻麦轮作系统作物水足迹效率及其驱动力研究

Authors: Wu, Mengyang (1); Cui, Simeng (1); Li, Yueyao (1); Xiao, Jianfeng (2); Cao, Xinchun (1, 3); Elbeltagi, Ahmed (4)

Author affiliation: (1) College of Agricultural Scienee and Engineering, Hohai University, Nanjing; 210098, China; (2) Zhejiang Water Resources and Hydropower Technology Consulting Center, Hangzhou; 310020, China; (3) Engineering Research Center for Agricultural Soil—Water Efficient Utilization, Carbon Sequestration and Emission Reduction, Nanjing; 210098, China; (4) Faculty of Agriculture, Mansoura University, Mansoura; 35516, Egypt

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 543-551

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Efficient and sustainable water use in agriculture, as viewed through the lens of water footprint analysis, plays a crucial role in enhancing regional food security and environmental sustainability. Focusing on the rice — wheat rotation System, a crop water footprint calculation model was developed based on water footprint theory. The model was applied to assess the efficiency of water use in the rice — wheat rotation System in Lianshui Irrigation District spanning from 1960 to 2019. The analysis revealed the temporal evolution and influencing factors of water use efficiency in this System. Results indicated that the generalized water System number was ranged from 0. 50 to 0. 76 over the studyperiod with a multi-year average of 0.65, showing no significant Overall trend. In contrast, the crop production water footprint exhibited a yearly average of 58.4 mVGJ, displaying a consistent decline. Specifically, the green water footprint accounted for 40. 6% ~ 80. 4% of the overall water footprint, while the blue water footprint averaged 22.6 m /GJ. Meteorological factors, predominantly precipitation, significantly influenced both the broad water System number and crop water footprint. The study highlighted a negative correlation between crop production water footprint and agricultural inputs, as well as regional Irrigation intensity. Factors such as agricultural mechanization and water-saving irrigation practices played a crucial role in shaping water use efficiency. Enhancing rainfall utilization and adopting advanced agricultural technologies were identified as effective strategies to optimize water resource management in agriculture. Findings from this research can offer valuable insights for developing regional agricultural water conservation Standards. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Crop rotation

Controlled terms: Agricultural implements - Agricultural robots - Cotton - Harvesters - Irrigation - Tractors (agricultural)

Uncontrolled terms: Agricultural water use - Agricultural water use efficiency - Blue-green water - Crop water footprint - Green water - Path analysis - Rice-wheat rotations - Water footprint - Water system - Water use efficiency

Classification code: 731.6 Robot Applications - 821.2 Agricultural Machinery and Equipment - 821.4 Agricultural Methods - 821.5 Agricultural Products

Numerical data indexing: Percentage 4.00E 00%, Percentage 6.00E 00%, Size 2.26E 01m

DOI: 10.6041/j.issn.1000-1298.2025.05.052

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

52. Improved YOLO v8 Method for Cucumber Fruit Segmentation in Complex Greenhouse Environments

Accession number: 20252118477639

Title of translation: 基于改进YOLO v8的复杂温室环境黄瓜果实分割方法

Authors: Xia, Tian (1); Xie, Chun (1); Li, Linyi (2, 3); Lu, Shenglian (4); Qian, Tingting (2, 5)

Author affiliation: (1) School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai; 201209, China; (2) Institute of Agricultural Scienee and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai; 201403, China; (3) Research Center of Shanghai Digital Agricultural Engineering and Technology, Shanghai; 201403, China; (4) School of Computer Science and Engineering, Cuangxi Normal University, Guilin; 541004, China; (5) Key Lahoratory of Smart Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai; 201403, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 433-442

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The detection and segmentation of cucumber fruits are crucial for phenotypic analysis and the management of cucumber growth. However, in complex greenhouse environments, fruits are often occluded by stems and leaves, and their color may be similar to the background, making it difficult for traditional methods to accurately identify fruit boundaries and achieve efficient segmentation. To address this issue, an improved YOLO v8-based method for cucumber fruit segmentation was proposed. This method incorporated deformable convolution network v4 (DCNv4) to enhance the model’s spatial adaptability and utilized the RepNCSPELAN4 module in combination with an additional C2F module to refine feature extraction and fusion, thereby improving the model’s segmentation Performance for cucumber fruit images in complex greenhouse environments. Experimental results showed outstanding Performance across multiple categories in two experimental settings; a glass greenhouse and a plastie greenhouse. Specifically, in the glass greenhouse scenario, the model aehieved a precision of 96. 3%, recall of 93. 1%, mean average precision (mAP50) of 96. 2%, and mAP50 — 95 of 85. 3%. In the plastie greenhouse scenario, the precision was 86. 8%, recall was 81. 9%, mAP50 was 90. 0%, and mAP50 — 95 was 77.0%. The proposed method demonstrated stronger robustness and generalization in handling boundary issues, multiple occlusions, and multi-scale segmentation, enabling the model to adapt to diverse and complex cultivation environments and accurately segment cucumber fruits. Accurate fruit image segmentation facilitated the acquisition of phenotypic parameters and provides reliable technical support for further phenotypic analysis of cucumber fruits, thereby promoting the application of agricultural phenotyping robots and the intelligent development of agricultural production. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Image segmentation

Controlled terms: Agricultural implements - Agricultural robots - Combines - Fruits - Harvesters - Intelligent robots

Uncontrolled terms: Cucumber - Deformable convolution - Features extraction - Features fusions - Fruit occlusion - Greenhouse environment - Images segmentations - Model segmentations - Phenotypic analysis - YOLO v8

Classification code: 101.6.1 Robotic Assistants - 731.6 Robot Applications - 821.2 Agricultural Machinery and Equipment - 821.5 Agricultural Products - 1106.3.1 Image Processing

Numerical data indexing: Percentage 0.00E00%, Percentage 1.00E00%, Percentage 2.00E 00%, Percentage 3.00E 00%, Percentage 7.70E 01%, Percentage 8.00E 00%, Percentage 9.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.041

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

53. Impact of Combined Straw and Biochar Application on Soil Nitrogen and Soybean Nitrogen Use Efficiency Dynamics in Northeast China’s Black Soil Region

Accession number: 20252118477641

Title of translation: 东北黑土区秸秆和生物炭混施对土壤氮素与大豆氮素利用的影响

Authors: Yang, Aizheng (1); Chi, Haocheng (1); Wang, Qiuju (2); Wang, Xiaofang (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: 5

Issue date: May 2025

Publication year: 2025

Pages: 523-533

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to investigate the effects of combined straw and biochar application on soil nitrogen pools, crop yield, and nitrogen utilization in the black soil region of Northeast China, a split-plot experimental design was employed, incorporating three field return methods—control (CK, no return), füll straw return (SF), and combined biochar and straw return (BS) —along with three nitrogen application rates; 75 kg/hm (Nl), 60 kg/hm (N2), and 45 kg/hm (N3). The results showed that the mixed application of straw and biochar alleviated the unfavorable impression of reduced nitrogen application on soil nitrogen content and soybean plant growth, and the effects were more pronounced at N2 nitrogen application. Compared with CK and SF treatments, BS treatment promoted the increase of soil ammonium nitrogen and nitrate nitrogen Contents by 7. 58% ~ 78. 08% and 19. 02% ~ 95. 56%, respectively, which significantly enhanced the net photosynthetic rate and nitrogen utilization efficiency of soybean, and led to the increase of soybean yield by 38.62% -60.97%. Comprehensive evaluation using the entropy-weighted TOPSIS model identified the BSN2 treatment as the most effective, achieving an average two-year yield of 3 058. 48 kg/hm with a nitrogen application rate of 60 kg/hm. This treatment also recorded high nitrogen use efficiency (0. 99), agronomic efficiency (9. 34 kg/kg), nitrogen recovery rate (0. 98), and nitrogen response index (2. 18). These findings can provide a scientific basis for optimizing nitrogen fertilizer management and enhancing straw utilization in Northeast China’s black soil region. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 38

Main heading: Agricultural implements

Controlled terms: Combines - Harvesters - Straw

Uncontrolled terms: Biochar - Black soil regions - Nitrogen application rates - Nitrogen pools - Nitrogen utilization - Nitrogen-use efficiency - Northeast China - Soil nitrogen - Soil nitrogen content - Soybean yield

Classification code: 821.2 Agricultural Machinery and Equipment - 821.6 Agricultural Wastes

Numerical data indexing: Mass 3.40E 01kg, Mass 4.50E 01kg, Mass 4.80E 01kg, Mass 6.00E 01kg, Mass 7.50E 01kg, Percentage 2.00E 00%, Percentage 3.862E 01% to 6.097E 01%, Percentage 5.60E 01%, Percentage 5.80E 01%, Percentage 8.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.050

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

54. Influence of Different Proportions of Wheat Replacing Com on Pelleting Characteristics of Mash Feed and Establishment of Prediction Model

Accession number: 20252118477988

Title of translation: 不同小麦替代玉米比例下混合物料制粒成型特性与预测模型研究

Authors: Yang, Jie (1, 2); Li, Xing (1); Qian, Guangyu (1); Li, Junguo (1, 2); Qin, Yuchang (3); Shao, Shuang (1); Li, Jun (1); Wu, Bencheng (4)

Author affiliation: (1) Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing; 100081, China; (2) Laboratory of Feed-derived Factor Risk Assessment for Animal Product Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing; 100081, China; (3) Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing; 100193, China; (4) Premix Business Unit, Anyou Biotechnology Croup Co., Ltd., Suzhou; 215437, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 569-576

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The Box — Behnken experimental design was used in the experiment, and a total of 17 groups of pelleting experiments were carried out. The results showed that with the increase of replaeement ratio of wheat, the bulk density, tap density, water solubility index, protein dispersibility index, pasting time of the mash feed were increased significantly (P © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Viscosity index

Uncontrolled terms: Conditioning temperatures - Conditioning time - Different proportions - Pellet quality - Pelleting characteristic - Power - Precision processing - Replacement ratio - Response surfaces methods - Wheat replacing com

Classification code: 1301.1.2 Physical Properties of Gases, Liquids and Solids

Numerical data indexing: Percentage 0.00E00%, Percentage 1.00E 02%, Percentage 5.00E 01%

DOI: 10.6041/j.issn.1000-1298.2025.05.055

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

55. Noise Reduction Method of Capacitive Cotton Seed Monitoring Signal Based on CEEMDAN Wavelet Threshold

Accession number: 20252118472031

Title of translation: 基于CEEMDAN-小波阈值的电容式棉种监测信号降噪方法

Authors: Yang, Miao (1, 2); Ren, Ling (1, 2); Wang, Shuang (1, 2); Li, Tao (1, 2); Zhang, Yuquan (1, 2)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 71-81

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problem that the signal generated in capacitive cotton seeding monitoring contained noise and thus the seeding information was not easy to be extracted, the CEEMDAN — wavelet threshold Joint noise reduction method was proposed. Firstly, according to the detection principle of cotton seedling quality, the noisy Simulation signal was constructed, and the empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) denoising effects of three traditional methods were compared on normal seeding, missed seeding, and repeat seeding Simulation signals. Secondly, the wavelet threshold denoising method was integrated into the CEEMDAN denoising method, and the threshold formula of the correlation coefficient was designed to differentiate a large number of intrinsic mode function (IMF) componented with a large number of noisy and IMF components with effective signals, and the noise in the noisy IMF components was removed and more of the shape characteristics of the original signal were retained, and the signal-to-noise ratio (SNR) of the omitted rebroadcasting was increased by 4. 950 9 dB and 6. 849 3 dB, respectively. The similarity of the curve (NCC) was increased by 0.028 0 and 0.054 9, and smoothness (SR) was decreased by 0.002 4 and 0. 004 5, respectively, which improved the problem of the poor noise reduction effect of the CEEMDAN denoising method alone on the omitted replay signal. Finally, a rowing signal acquisition test platform was built to validate the proposed method, and the results showed that the method had good noise reduction and signal feature reduction capability, and after noise reduction, it implemented a noise reduction effect on the number of distinguished seeds. The results showed that the method had good noise reduction and signal feature reduction ability, and the noise reduction could realize monitoring of number of sown seeds. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Empirical mode decomposition

Controlled terms: Noise abatement - Signal denoising - Signal to noise ratio

Uncontrolled terms: % reductions - Adaptive noise - Capacitive cotton seed monitoring signal - Complementary ensemble empirical mode decomposition with adaptive noise denoising - Cotton seeds - Empirical Mode Decomposition - Monitoring signals - Noise denoising - Toothed plate dibblers - Wavelet threshold de-noising

Classification code: 716.1 Information Theory and Signal Processing - 1106.3 Digital Signal Processing - 1502.1.1.4 Pollution Control

Numerical data indexing: Decibel 3.00E 00dB, Decibel 9.00E 00dB

DOI: 10.6041/j.issn.1000-1298.2025.05.007

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

56. Pig Lameness Detecting Method Based on Key Points and Walking Features

Accession number: 20252118477604

Title of translation: 基于关键点和步行特征的猪只跛行检测方法

Authors: Yang, Qiumei (1, 2); Huang, Senpeng (1, 2); Xiao, Deqin (1, 2); Hui, Xiangyang (1, 2); Huang, Yigui (1, 2); Li, Wen’gang (1, 2)

Author affiliation: (1) College of Mathematies and Information, South China Agrieultural Vniversity, Guangzhou; 510642, China; (2) Key Laboratory of Smart Agrieultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, Guangzhou; 510642, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 466-474

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The problem of lameness in pigs presents significant ehallenges to the produetion and management of pig farms, making accurate detection of pig lameness crucial. Currently, pig farms primarily rely on manual Observation and recording, which is inefficient, time-consuming, and prone to subjective judgment errors. In light of this, a method for detecting pig lameness based on key points and Walking charaeteristics was proposed. Firstly, key point Information for pigs was defined and determined, including critieal parts such as the legs, knees, and back. Based on these key points, an improved YOLO v8n — pose model was employed for detection. This model built upon the original YOLO v8n —pose by introducing a bidirectional feature pyramid network (BiFPN) at the neck for multi-scale feature fusion and incorporating a RepGhost network into the backbone to reduce the parameter count and computational complexity of the feature extraction network. Then using the coordinates of the detected key points, Walking charaeteristics such as stride length, knee bending degree, and back curvature were calculated. These features were inputed into a K-nearest neighbors (KNN) algorithm to classify pigs as lame or non-lame. Experimental results showed that the improved YOLO v8n — pose model achieved a mean average precision (mAP) of 92. 4%, which was 4. 2 percentage points higher than the detection aecuraey of the original YOLO v8n — pose model. Compared with other key point detection models (HRNet — w32, Lite— HRNet, ResNet50, ViPNAS, and Hourglass), the mAP was improved by 10. 2, 11.6, 14.2, 11.8 and 12. 5 percentage points, respectively. The KNN algorithm achieved a detection aecuraey of 81. 7% on the pig lameness test set, which was 1.5, 11.3 and 6.5 percentage points higher than that of the BP algorithm, Decision Tree algorithm, and SVM algorithm, respectively. These results demonstrated that the proposed method for detecting pig lameness was feasible and can provide technical support for pig farm detection. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Nearest neighbor search

Controlled terms: Agricultural implements - Combines - Harvesters - NP-hard - Orchards - Random forests

Uncontrolled terms: Gait features - Keypoint deteetion - Keypoints - Lameness - Nearest-neighbor algorithms - Percentage points - Pig - Pig farms - Pose models - YOLO v8n — pose

Classification code: 821.2 Agricultural Machinery and Equipment - 821.4 Agricultural Methods - 1101.2 Machine Learning - 1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory - 1201.7 Optimization Techniques

Numerical data indexing: Percentage 4.00E 00%, Percentage 7.00E 00%

DOI: 10.6041/j.issn.1000-1298.2025.05.044

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

57. Design and Experiment of Seed Orientation Correction Element for High-speed Belt-type Soybean Seeding Device

Accession number: 20252118472029

Title of translation: 带式大豆高速导种装置籽粒姿态矫正元件设计与试验

Authors: Yi, Shujuan (1); Wang, Guangyu (1); Li, Yifei (1, 2); Wang, Song (1); Li, Shuaifei (1); Wei, Ruiyong (1)

Author affiliation: (1) College of Engineering, Heilongjiang Bayi Agrieultural Vniversity, Daqing; 163319, China; (2) College of Engineering, Northeast Agrieultural 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: 5

Issue date: May 2025

Publication year: 2025

Pages: 268-278 and 424

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issue of unstable seed orientation and inconsistent seed positions within the single-seed Chamber of a belt-type seed delivery device during high-speed seeding (13-16 km/h), a seed orientation correction element for the belt-type seed delivery device was designed. This element consisted of five parallel transverse ridges, each with a height of 1 mm, and was made from nitrile rubber. When soybeans came into contact with the first ridge, they were adjusted so that their long axis was oriented perpendicularly to the direction of the seed belt movement, thereby ensuring stable seed delivery. By analyzing the State of the seeds during the collision deformation phase and the collision recovery phase, the theoretical two-dimensional seed delivery position within the correction zone was clearly defined. Using the EDEM discrete element Simulation Software, a Simulation experiment was conducted to determine the optimal height of the correction ridges. The qualification rate of seed inclination and the qualification rate of seed displacement were used as evaluation indicators. Through single-factor experiments, the displacement trajectory of the seeds from the seed-limiting to the correction phase was analyzed, clarifying the effect of the correction ridge height on the lateral movement of the seeds. The results showed that with a ridge height of 1.00 mm, the average qualification rate of seed inclination was 95. 7%, and the average qualification rate of seed displacement was 98. 2%. High-speed camera technology was used to conduct single-factor comparative experiments, with the orientation variability coefficient and plant spacing Variation coefficient used as indicators to compare the correction effects. The comparative experiments demonstrated that the belt-type seed delivery device equipped with the correction element had lower orientation variability coefficients and plant spacing Variation coefficients than the device without the correction element. With the correction element featuring a ridge height of 1 mm, the average orientation variability coefficient was 16. 45%, and the average plant spacing Variation coefficient was 12. 78%, meeting the requirements for high-speed precision seeding Operations. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Computer software selection and evaluation

Controlled terms: Belts

Uncontrolled terms: Belt-type seed guide device - Correction elements - Delivery device - Directional seed guidance - Guide device - High Speed - High-speed precision planter - Seed correction - Seed orientation - Soybean

Classification code: 601.2 Machine Components - 602.2 Mechanical Transmissions - 1106 Computer Software, Data Handling and Applications

Numerical data indexing: Percentage 2.00E 00%, Percentage 4.50E 01%, Percentage 7.00E 00%, Percentage 7.80E 01%, Size 1.00E-03m, Size 1.30E 04m to 1.60E 04m

DOI: 10.6041/j.issn.1000-1298.2025.05.025

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

58. Improved YOLO v8n for Detection of Hangzhou White Chrysanthemum in Unstructured Environments

Accession number: 20252118477615

Title of translation: 基于改进YOLO v8n的非结构环境下杭白菊检测方法

Authors: Yu, Chennan (1, 2); Wu, Yonghong (1); Zhou, Jie (1); Yao, Kun (1); Huan, Xiaolong (1, 2); Chen, Jianneng (1, 2)

Author affiliation: (1) School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou; 310018, China; (2) Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture, Hangzhou; 310018, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 405-414

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In unstructured environments, the Cluster growth characteristics of Hangzhou white Chrysanthemum lead to severe mutual occlusion, reducing detection accuracy for Chrysanthemum detection algorithms. To address this issue, an improved YOLO v8n detection model for Hangzhou white Chrysanthemum, called Hangzhou white Chrysanthemum — YOLO v8n (Hwc — YOLO v8n), was proposed. Firstly, the model’s ability was enhanced to finely detect critical, similar features of the Chrysanthemum by increasing the label categories from two to three. Secondly, a dynamic feature extraction module (C2f — Dynamic) was designed in the backbone network to strengthen the model’ s adaptive response to missing features in occluded targets. Additionally, a 160 pixel x 160 pixel detection head was added to the detection head section, allowing the model to detect small targets more effectively. Finally, the angle penalty metric loss (SIoU) was adopted to optimize the bounding box loss function, improving both detection accuracy and generalization capability. Experimental results from module placement and heatmap analysis demonstrated that the C2f — Dynamic module can dynamically adapt to feature changes in occluded targets. The improved Hwc — YOLO v8n model achieved a 1. 7 percentage points increase in mean average precision and a 0. 88 percentage points increase in mean recall rate for the occluded Hangzhou white Chrysanthemum. Ablation and comparison experiments showed that the improved Hwc - YOLO v8n outperformed DETR, SSD, YOLO v5, YOLO v6, and YOLO v7 in detection of the Chrysanthemum. Specifically, compared with DETR, SSD, YOLO v5, YOLO v6, and YOLO v7, the mAP was improved by 5. 7, 12. 6, 0. 7, 0. 75, and 11. 25 percentage points, respectively. The mR was increased by 2. 15 percentage points and 1.4 percentage points compared with that of YOLO v5 and YOLO v7, respectively. The research result can provide a technical foundation for future intelligent harvesting of Hangzhou white Chrysanthemum. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Object detection

Controlled terms: Image recognition - Object recognition - Optical character recognition

Uncontrolled terms: Cluster growth - Detection accuracy - Growth characteristic - Hangzhou - Hangzhou white chrysanthemum - Objects detection - Occlusion detection - Percentage points - Unstructured environments - YOLO v8

Classification code: 741.1 Light/Optics - 1106.3.1 Image Processing - 1106.5 Computer Applications - 1106.8 Computer Vision

DOI: 10.6041/j.issn.1000-1298.2025.05.038

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

59. Design and Test of Temperature Control System for White Pepper Curing Based on SSAPSO PID

Accession number: 20252118477991

Title of translation: 基于SSAPSO-PID的白胡椒熟化温度控制系统设计与试验

Authors: Yu, Guoyan (1); Zhang, Jiawei (1, 2); Zhang, Yuan (2, 3); Wei, Lijiao (2, 4); Zhao, Zhenhua (2, 5); Shen, Dezhan (2, 5)

Author affiliation: (1) School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang; 524091, China; (2) Agricultural Machinery Research Institute, Chinese Academy of Tropical Agricultural Sciences, Zhanjiang; 524091, China; (3) Guangdong Engineering Technology Research Center of Precision Emission Control for Agricultural Particulates, Zhanjiang; 524000, China; (4) Zhanjiang Key Laboratory of Dynamics and Precision Emission Control for Particulates, Zhanjiang; 524091, China; (5) Key Laboratory of Agricultural Equipment for Tropical Crops, Ministry of Agriculture and Rural Affairs, Zhanjiang; 524091, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 5

Issue date: May 2025

Publication year: 2025

Pages: 589-596

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of prolonged inability to maintain constant temperature eontrol and excessive reliance on manual assistance in the curing phase of white pepper primary processing production lines, a proportion integration differentiation (PID) -based control System was developed to control the curing temperature of the white pepper during processing. It is a high demand to maintain the constant curing temperature. Specifically, too high curing temperature can lead to the internal physicochemical properties of the destruction, whereas, too low curing temperature can lead to curing not complete, which makes the peeling rate decreased. The control System with an ST Microelectronics 32-bit Microcontroller (STM32) and a touchscreen was utilized to control the start/stop of the steam generator and the opening of the electric regulating valve. A temperature sensor was installed at the outlet of the curing machine, and a PT100 temperature sensor was employed to collect the curing temperature in real-time. Subsequently, the collected data was fed back to the STM32 microcontroller. The PID closed-loop control algorithm was applied to calculate the actuator, adjusting parameters appropriately to ensure stable control of the curing temperature by modulating the steam flow.A systematic analysis of the convective heat exehange process between white pepper and steam at temperature was conducted. A theoretical model of heat transfer was established by using the step response curve method, and the data curve was processed (R =0. 969) to derive the control model for the temperature inside the curing machine over time. Simulation analysis was performed by using the Simulink platform to determine the optimal parameters for PID control. Response curves from four PID parameter tuning methods, including the Ziegler — Nichols method, the decay curve method, the critical proportional method, and the sparrow search algorithm-based particle swarm optimization method (SSAPSO), were compared. Ultimately, it was found that the SSAPSO-based method yielded the best control effect in terms of dynamic Performance indicators with PID parameters (proportional coefficient K = 0. 875 9, integral coefficient Kt = 0. 02, and differential coefficient Kd = 4. 325 5). The response time of the PID Controller obtained by the SSAPSO-based method was approximately 40 s with an overshoot of about 2. 5%. Systematic experimental studies demonstrated that throughout the entire 8 minutes curing process, the current curing temperature was sampled every minute. Due to direct convective heat exehange between the curing machine and the air, the temperature remained stable within the ränge of (99 ± 1. 5) °C. The average relative error of the curing temperature was less than 1. 2%, and the coefficient of Variation was less than 1. 3%, thereby achieving automated, precise, and efficient temperature control during the curing process. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Particle swarm optimization (PSO)

Controlled terms: Boiler circulation - Boiler firing - Electric heating - Low temperature operations - Natural convection - Reboilers

Uncontrolled terms: Curing temperature - Particle swarm optimization algorithm - Primary processing - Processing lines - Proportion integration differentiation control - Proportion integration differentiations - Search Algorithms - Sparrow search algorithm - White pepper primary processing line - White peppers

Classification code: 302.2 Heat Transfer - 303.1 Process Heating - 305.4 Cryogenics - 306.1 Heat Exchange Equipment and Components - 1002.1 Steam Power Plant Equipment and Operation - 1106 Computer Software, Data Handling and Applications - 1201.7 Optimization Techniques