2025年第2期

2025年第2期

2025年第2期共收录50篇

1. Obstacle Avoidance Planning of Citrus Picking Robot in Natural Environment

Accession number: 20251018008459

Title of translation: 自然环境下柑橘采摘机器人避障规划研究

Authors: Bao, Xiulan (1, 2); Bao, Yougang (1); Ma, Xiaojie (1); Ma, Zhitao (1); Ren, Mengtao (1); Li, Shanjun (1, 2)

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

Corresponding author: Li, Shanjun(shanjunlee@mail.hzau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 420-428

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the dense and randomly positioned growth of citrus branches, leaves, and fruits, to achieve safe interactive picking of interlaced and overlapping branches and fruits, a citrus obstacle avoidance picking method was proposed. To enhance the positioning accuracy and picking efficiency, the hand-eye calibration problem was transformed into solving the equation TtX = XT2, completing the calibration from the camera coordinate System to the base coordinate System of the robotic arm. For the citrus open-center tree shape in the natural environment, tree skeleton extraction based on point cloud density was conducted, and noise reduction processing of the branch and trunk point clouds was performed through the point density threshold method to increase the Operation speed. The octree map method was utilized to construct the obstacle map of branches and trunks, and the hierarchical bounding box method was employed to fit the robotic arm and carry out collision detection. With the objective of time optimization, an improved RRT - connect obstacle avoidance planning algorithm that conforms to the agricultural requirements of picking was proposed. Target bias was introduced to the RRT - connect algorithm for optimizing and guiding the sampling points. To verify the feasibility of this obstacle avoidance method, taking the citrus orchard with Standard dwarf and dense planting cultivation as the research object, an obstacle avoidance System for the picking robot was established. Multiple sets of obstacle avoidance picking experiments were respectively conducted for citrus fruits growing inside the fruit tree and close to the trunk in the natural environment. The experimental results indicated that the obstacle avoidance movement time for fruits growing close to the trunk was 9.5s, and the success rate of obstacle avoidance picking was 91%; for fruits growing inside the fruit tree, the obstaole avoidance movement time was 10. 5 s, and the success rate of obstaole avoidanoe pioking was 88%. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Citrus fruits

Controlled terms: Agricultural robots? - ?Conformal mapping? - ?Gluing? - ?Noise abatement? - ?Orchards? - ?Robot programming? - ?Robotic arms

Uncontrolled terms: Co-ordinate system? - ?Fruit trees? - ?Movement time? - ?Natural environments? - ?Obstacle avoidance planning? - ?Obstacles avoidance? - ?Oitrus? - ?Picking robot? - ?Pioking robot? - ?Point-clouds

Classification code: 101.6.1 Robotic Assistants? - ?1106.1 Computer Programming? - ?1201.14 Geometry and Topology? - ?1201.2 Calculus and Analysis? - ?1502.1.1.4 Pollution Control? - ?210 Adhesive Materials? - ?214 Materials Science? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products

Numerical data indexing: Percentage 8.80E+01%, Percentage 9.10E+01%, Time 5.00E+00s, Time 9.50E+00s

DOI: 10.6041/j.issn.1000-1298.2025.02.039

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

2. Structural Design and Test of Supported Paddy-field Leveling Machine

Accession number: 20251018008994

Title of translation: 支撑式水田平地机结构设计与试验

Authors: Chen, Gaolong (1); Hu, Lian (1, 2); Wang, Pei (1, 2); Zhao, Runmao (1, 2); Feng, Dawen (1); Tian, Li (1); Huang, Zhicheng (1); Chen, Yuqi (1); Wang, Jingting (1)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) State Key Laboratory of Agricultural Equipment Technology, Guangzhou; 510642, China

Corresponding author: Zhao, Runmao(rmzhao@scau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 252-260 and 274

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The leveling of paddy fields is an indispensable part of rice production. Aiming to improve the adaptability of existing leveling machines to the bumpy hard bottom layer of paddy fields and further enhance the operational performance, the supported paddy-field leveling implement was developed. Firstly, according to the working principle of the leveling shovel, the height adjustment mechanism of the leveling shovel was kinematically analyzed. On this basis, the leveling shovel and its height adjustment mechanism were designed. Secondly, the supported rod structure was designed, and the influence of the supported rods on the motion characteristics of the leveling shovel was studied. Finally, a comparison test with / without supported rods and a paddy-field leveling test were carried out. The results of the comparison test showed that the amplitude of height change of the leveling shovel with the supported rods was reduced by more than 15% throughout the test, and reduced by more than 30% in the convex position of the field surface. Meanwhile, the number of height changes of the leveling shovel was reduced. It showed that the leveling shovel with supported rods was more conducive to the height control of leveling shovels and was more suitable for the operation of bumpy hard bottom layer of paddy fields. The leveling test results for 0. 21 hm2 of paddy field showed that the standard deviation (Sd ) of the topography height was 21. 66 mm, and the proportion (ρ) of points (| hi - h | ≤ 30 mm, where hi indicated the height of each measuring point, and h indicated the average height of all measuring points.) was 86. 54% . The test results for two paddy fields with a total area of 1. 89 hm2 showed that the standard deviations Sd for the two fields were 26. 02 mm and 27. 43 mm, and the proportions ρ were 80. 53% and 81. 03%, respectively. After leveling, the standard deviation Sd in all test fields was less than 30 mm, and the proportion ρ was higher than 80%, which met the requirements of paddy-field leveling, and verified the structural advantages and design validity of the supported paddy-field leveling machine. This can provide equipment support for mechanized leveling of paddy fields with bumpy hard bottom layer. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Main heading: Shovels

Controlled terms: Agricultural robots? - ?Control rods? - ?Leveling (machinery)? - ?Voltage dividers

Uncontrolled terms: Adjustment mechanisms? - ?Bottom layers? - ?Field topography? - ?Hard bottoms? - ?Leveling machines? - ?Levelings? - ?Paddy fields? - ?Paddy-field leveling? - ?Standard deviation? - ?Supported rod

Classification code: 1001.1 Nuclear Power Plant Equipment and Operation? - ?1001.2 Nuclear Reactors? - ?605.2 Small Tools, Unpowered? - ?704.1 Electric Components? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Percentage 1.50E+01%, Percentage 3.00E+00%, Percentage 3.00E+01%, Percentage 5.30E+01%, Percentage 5.40E+01%, Percentage 8.00E+01%, Size 2.00E-03m, Size 3.00E-02m, Size 4.30E-02m, Size 6.60E-02m

DOI: 10.6041/j.issn.1000-1298.2025.02.024

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

3. Local Path Dynamic Programming Algorithm for Automatic Row Alignment Traveling of Wine Grape Harvester

Accession number: 20251018008468

Title of translation: 酿酒葡萄收获机自动对行驾驶局部路径动态规划算法

Authors: Dai, Zhen (1); Guo, Yanchao (2); Wang, Xiaole (1); Zhang, Zhining (1); Dai, Baobao (1); Yang, Yang (1); Zhang, Tie (3); Chen, Liqing (1)

Author affiliation: (1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) State Key Laboratory of Intelligent Agricultural Power Equipment, Luvyang, 471039, China; (3) Chinese Academy of Agricultural Mechanization Sciences Croup Co., Ltd., Beijing; 100083, China

Corresponding author: Wang, Xiaole(wangxiaole@ahau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 124-135

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate row alignment harvesting of grapes can effeotively reduce the oollision between Vibration meohanism of the harvester and the trellis, which is an important means to aehieve large-seale mechanized harvesting. Based on the loeal driving seene model between grape rows in Frenet coordinate System, an automatic row alignment path planning algorithm for grape harvesters was proposed. Using the global Operation path as a reference line, the algorithm utilized onboard LiDAR to identify grape rows ahead in real time, and applied the K-means algorithm to cluster the point cloud of grape rows. The Lattiee algorithm was used to dynamieally sample the driving area ahead according to the traveling speed, and then the local path Clusters were generated based on fifth-order polynomials. The extreme steering positions of the front and rear wheels were taken as the feature points of the harvester, and then the collision detections were conducted between feature points and the lateral segmentation minimum bounding rectangle of grape rows, and the offset costs of each local path relative to grape rows and the global path were calculated. Based on the operating states and environment oondition, the deoision limits of the grape line deviating from the reference line were determined, and the weighted sum of the offset costs were optimized by dynamie programming algorithm, and then the path with the minimum cost in the path cluster ean be obtained as the current loeal path. The algorithm was validated through Simulation by using the robot Simulation Software Gazebo and Rviz, as well as real experimental tests. The results showed that the average lateral error of the planned local path relative to grape rows was 4. 37 cm, and the maximum absolute curvature was 0. 201 1 m~. When the global path deviated significantly from the grape row, the local path can effectively correct the deviation and meet the driving requirements for grape harvesting Operations. In the Simulation test for planning a path of 6 m, the average processing time of this algorithm was 213 ms per iteration, with a maximum of 337 ms per iteration. In the experimental test for planning a path of 6 m, the average processing time was 577 ms per iteration, with a maximum of 816 ms per iteration. The relevant research methods can provide reference for local path planning of agricultural machinery in vineyard scenarios. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 20

Main heading: Motion planning

Controlled terms: Alignment? - ?Conformal mapping? - ?Data flow analysis? - ?Health risks? - ?K-means clustering? - ?Polynomials? - ?Public risks? - ?Risk analysis? - ?Risk assessment? - ?Risk perception ? - ?Software testing

Uncontrolled terms: Costs Optimization? - ?Dynamic programming algorithm? - ?Dynamie sprinkling sampling? - ?Experimental test? - ?Grape harvesters? - ?Offset cost optimization? - ?Path offset calculation? - ?Processing time? - ?Reference lines? - ?Row alignment path planning

Classification code: 102.1.2.1 Health Care? - ?1101 Artificial Intelligence? - ?1106.2 Data Handling and Data Processing? - ?1106.9 Computer Software? - ?1108 Security and Privacy? - ?1201.1 Algebra and Number Theory? - ?1201.14 Geometry and Topology? - ?1201.2 Calculus and Analysis? - ?1202 Statistical Methods? - ?601.1 Mechanical Devices? - ?901.3 Engineering Research? - ?903.1 Information Sources and Analysis? - ?914.1 Accidents and Accident Prevention

Numerical data indexing: Size 1.00E00m, Size 3.70E-01m, Size 6.00E+00m, Time 2.13E-01s, Time 3.37E-01s, Time 5.77E-01s, Time 8.16E-01s

DOI: 10.6041/j.issn.1000-1298.2025.02.012

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

4. Parameter Optimization and Testing of Apple Laser Flower Thinning Test Bed Based on LT YOLO Inspection and Machine Vision

Accession number: 20251018008497

Title of translation: 基于LT-YOLO检测与机器视觉的苹果激光疏花试验台参数优化与试验

Authors: Gao, Ang (1); Wu, Kun (2); Song, Yuepeng (1); Ren, Longlong (1); Ma, Wei (3); Liu, Yilin (1, 4)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian; 271018, China; (2) Department of Traffic Engineering, Shandong Transport Vocatwnal College, Weifang; 261206, China; (3) Institute of IJrban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu; 610213, China; (4) College of Engineering, Heilongjiang Bayi Agricultural University, Datpng, 163316, China

Corresponding author: Song, Yuepeng(uptonsong@sdau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 393-401

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Laser flower thinning technology, as an emerging and promising technology in the field of smart orchard management, still faeed eritical challenges in optimizing laser parameters and achieving preeise apple flower deteetion. Aiming at the optimization of key parameters in apple blossom laser flower thinning technology, a laser flower thinning test stand was designed, and the height of the test stand, the laser striking time and the PWM duty cycle were optimized by the orthogonal test method to obtain the optimal parameter eombinations: a laser height of 20 em, a striking time of 10 s and a laser power (PWM duty cycle) of 50% would achieve the best flower thinning effect. For apple flower identification and localization in laser flower thinning, the lightweight and targeted - you only look once (LT — YOLO) apple flower deteetion model was proposed, the DPRViTBlock module based on ViTBlock and the DPRVBC2f module based on the C2f module were designed, and the ELA attention module of the DPRVBC2f module was added, which was applied in the feature extraction of the deteetion backbone and the deteetion head to enhance the apple blossom deteetion Performance, Validation focused on the aecuraey, recall and average mean of the model were 83. 16%, 82. 15% and 87.47%, respectively, compared with that of the YOLO v8 model it was improved by 5. 04 percentage points, 2. 12 percentage points and 2. 15 percentage points, respectively. The model size was 5. 26 MB and the deteetion speed was 128 f/s, which met the aecuraey and real-time requirements for use. The research result can provide a scientific basis for the further optimization and intelligent application of apple flower thinning technology. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Orchards

Controlled terms: Dynamic programming? - ?Linear programming? - ?Nonlinear programming

Uncontrolled terms: Apple flower? - ?Duty-cycle? - ?Laser flower thinning? - ?LT - YOLO? - ?Parameter optimization? - ?Percentage points? - ?Target deteetion? - ?Test stands? - ?Thinnings

Classification code: 1201.7 Optimization Techniques? - ?821.4 Agricultural Methods

Numerical data indexing: Time 1.00E+01s, Percentage 1.50E+01%, Percentage 1.60E+01%, Percentage 5.00E+01%, Percentage 8.747E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.036

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

5. Curve Path Tracking Control of Agricultural Machinery Automatic Driving Based on Füll State Feedback Control

Accession number: 20251018008490

Title of translation: 基于全状态反馈控制的农机自动驾驶曲线路径跟踪方法

Authors: He, Jie (1, 2); Liu, Shanqi (1, 2); Man, Zhongxian (1, 2); Yue, Mengdong (1, 2); Wang, Jingting (1, 2); Wang, Pei (1, 2); Hu, Lian (1, 2)

Author affiliation: (1) Key Laboratory of Key Technologly on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou; 510642, China; (2) State Key Laboratory of Agricultural Equipment Technology, Guangzhou; 510642, China

Corresponding author: Hu, Lian(lianhu@scau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 145-154

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Working on irregul?r farmland along a curved path oan effeotively improve the applicability of unmanned agricultural machinery Operations. Aiming at the problem of low coverage of unmanned curved farmland Operations, a curved path tracking control method was proposed based on full-state feedback control. A third-order full-state feedback Controller with lateral deviation, heading deviation and heading increment as State vectors was constructed. The System was linearized and converted into a spatial matrix form. The gain matrix was solved under the condition of satisfying the System stability constraint to obtain the front wheel steering angle. The front wheel steering angle was compensated according to the curvature of the nearest path point. The Controller Performance was verified based on the Matlab/Simulink Simulation environment. The results showed that the average absolute error after compensation was reduced by 31% compared with that of the algorithm before compensation, and the maximum absolute error was reduced by 17. 9%. Three sets of field experiments were designed to verify the proposed method. The test results of variable curvature path showed that when the operating speed of agricultural machinery was 0. 7 m/s, the maximum lateral deviation was 0. 0705 m, the absolute error was 0. 021 8 m, the Standard deviation was 0. 023 4 m, the maximum heading deviation was 12. 25°, the absolute error was 1.37°, and the Standard deviation was 1.68°; the test results of fixed curvature path showed that the maximum lateral deviation was 0. 103 4 m, the absolute error was 0. 042 4 m, the Standard deviation was 0. 047 7 m, the maximum heading deviation was 8. 9°, the absolute error was 1. 86°, and the Standard deviation was 2. 45 °; the results of traeking test along curved ridges showed that the maximum lateral deviation was 0. 059 7 m, the absolute error was 0. 012 3 m, the Standard deviation was 0. 015 8 m, the maximum heading deviation was 7.01°, the absolute error was 1. 85°, and the Standard deviation was 2. 49°. The traeking accuracy can meet the operational requirements. The researeh results can provide theoretical and technieal support for unmanned agricultural maehinery to perform eomplex and changeable curved edge Operations. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: MATLAB

Controlled terms: Agricultural robots? - ?Error compensation? - ?Feedback control? - ?Fertilizers? - ?Matrix algebra? - ?State feedback? - ?Wheels

Uncontrolled terms: Absolute error? - ?Agricultural maehinery? - ?Autopilot? - ?Control state? - ?Curve navigation? - ?Full state feedback? - ?Full-state feedback control state feedback? - ?Path traeking? - ?Standard deviation? - ?State feedback control

Classification code: 1106.5 Computer Applications? - ?1201.1 Algebra and Number Theory? - ?1201.5 Computational Mathematics? - ?1502.1.1.3 Soil Pollution? - ?601.2 Machine Components? - ?731 Automatic Control Principles and Applications? - ?731.1 Control Systems? - ?731.1.1 Error Handling? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals

Numerical data indexing: Percentage 3.10E+01%, Percentage 9.00E+00%, Size 3.00E+00m, Size 4.00E+00m, Size 7.00E+00m, Size 7.05E+02m, Size 8.00E+00m, Velocity 7.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2025.02.014

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

6. Analysis of Lateral Seepage in Paddy Field with Controlled Irrigation and Drainage Regulation

Accession number: 20251018008484

Title of translation: 灌排调控下稻田水分侧渗过程规律分析

Authors: He, Yupu (1); Wan, Jiawei (1); Wang, Rongyong (2); Qi, Wei (1); Ji, Renjing (1); Mai, Zijun (1)

Author affiliation: (1) Department of Rural Water Management, Nanjing Hydraulic Research Institute, Nanjing; 210017, China; (2) Department of Water Resources Bureau of Qihe County, Qihe; 251199, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 463-473

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The process of lateral seepage constitutes a crucial component of the water cycle in paddy fields. Previous studies have foeused on the effects of control regulation field water layer management on lateral seepage in paddy fields. However, less attention has been paid to the lateral seepage of paddy field water under controlled irrigation, and there is a paueity of research findings regarding seepage in paddy fields under coupled controlled drainage. In order to optimize the water management of paddy field, a two-year in-situ field test was conducted, and diverse irrigation and drainage treatments were implemented. The characteristics of lateral seepage in paddy fields subjected to controlled irrigation and drainage regulation were systematically analyzed, and the response mechanism of lateral seepage to such control regulation was revealed. The findings were as follows: compared with free drainage, controlled drainage inhibited the change of soil moisture content in the field bund. Compared with flooding irrigation, the fluctuation of soil moisture content in the field bund under controlled irrigation field bund was more intense. Controlled irrigation and drainage regulation had a direct impact on the characteristics of lateral seepage in the field - bund - canal transition zone, and significantly reduced the total amount of lateral seepage. Controlled irrigation and drainage, as opposed to flooding irrigation and free drainage, respectively reduced the peak and mean values of lateral seepage intensity, and significantly reduced the total amount of lateral seepage in the paddy field, during the two-year experiment period, the controlled irrigation and drainage treatment of the riee fields resulted in a reduction of 63. 49% eompared with the free irrigation approach. Both paddy field irrigation and drainage treatments exerted a considerable influence on lateral seepage intensity, with irrigation treatments demonstrating a stronger effect. Paddy field irrigation treatment and drainage treatment had a signifieant effeet on the lateral seepage intensity, and the effeet of irrigation treatment was stronger. Compared with flooding irrigation, eontrolled irrigation signifieantly increased the proportion of water seeping from the paddy field - ridge - channel area. To maximize irrigation effeets, it was imperative to strengthen management of water lateral seepage within the fields during eontrolled irrigation implementation. Lateral seepage in paddy fields subjeeted to irrigation and drainage regulation primarily oeeurred in the field bund soil at a depth of 10 ~ 20 em. Water pereolation ehannels were presented at depths of 10 ~20 em below the ground surfaee. By implementing effective waterproofing measures at this depth, it ean signifieantly reduee the loss of field moisture. The researeh revealed the lateral seepage process and its responsiveness to soil moisture changes in the paddy field _ bund _ canal transition zone under irrigation and drainage regulation. The insights gained aim to serve as a referenee for the effieient utilization and metieulous management of agrieultural water resources. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 47

Main heading: Soil moisture

Controlled terms: Drainage? - ?Irrigation canals? - ?Seepage? - ?Subirrigation

Uncontrolled terms: Alternate wetting? - ?Controlled irrigations? - ?Controlled-drainage? - ?Floodings? - ?Irrigation and drainage regulation? - ?Irrigation treatments? - ?Lateral seepage? - ?Paddy fields? - ?Soil moisture content? - ?Transition zones

Classification code: 444 Water Resources? - ?444.1 Surface Water? - ?446.1 Water Supply Systems? - ?483 Soil Mechanics and Foundations? - ?483.1 Soils and Soil Mechanics? - ?821.4 Agricultural Methods

Numerical data indexing: Percentage 4.90E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.043

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

7. Design and Testing of Sprayer Nozzle Performance Testing Instrument

Accession number: 20251018008495

Title of translation: 基于多传感器同步采集的喷雾机喷嘴性能检测仪设计与试验

Authors: Hu, Jun (1, 2); Feng, Chao (1, 2); Liu, Changxi (1, 2); Li, Yufei (1, 2); Shi, Hang (1, 2)

Author affiliation: (1) College of Engineering, Heilongjiang Bayi Agricultural University, Daqing; 163319, China; (2) Key Laboratory of Soybean Mechanized Produktion, Ministry of Agriculture and Rural Affairs, Daqing; 163319, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 305-313

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The failure of nozzle Performance is currently the most common and critical issue in plant protection Operations. It directly affects the efficacy of pesticide spraying, consequently, has a significant impact on the quality and efficiency of plant protection tasks. Aiming to develop a nozzle Performance failure detector for sprayers, enabling accurate evaluation of nozzle failure before field Operations and ensuring efficient and precise plant protection. The System used an STM32 microcontroller as the core processing unit, integrating high-precision flow and pressure sensors to collect real-time data from the nozzle tip. The data was transmitted via Bluetooth to the Lab VIEW platform, facilitating efficient, visual data analysis, early warnings, and storage to meet real-time nozzle Performance monitoring requirements. The experimental results showed that for failed nozzle pressure detection, the maximum pressure measurement error was 2. 174%, and the maximum flow measurement error was 1. 936%. During the simultaneous Performance testing of multiple nozzles, the maximum relative pressure measurement error was 1. 515%, with an average error of 0. 887%, while the maximum relative flow measurement error was 2.061%, with an average error of 1. 151%. During field Validation tests of multiple nozzle Performances, the maximum relative pressure measurement error was 1. 990%, with an average error of 1.629%. The maximum relative flow measurement error was 2.713%, with an average error of 2. 014%, meeting the requirements for nozzle Performance testing. This deviee provided a reliable and efficient intelligent method for deteeting nozzle Performance failures, meeting the technical requirements for pre-operational testing in standardized plant protection tasks. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Flow measurement

Controlled terms: Flowmeters? - ?Measurement errors? - ?Network security? - ?Pressure measurement? - ?Pressure sensors? - ?Pressure transducers? - ?Spray nozzles? - ?Steganography? - ?Strain measurement? - ?Velocity measurement

Uncontrolled terms: Average errors? - ?Flow measurement errors? - ?Flow-sensors? - ?Lab VIEW? - ?Nozzle performance testing? - ?Performance? - ?Performance testing? - ?Plant protection? - ?Sprayer? - ?STM32

Classification code: 1106 Computer Software, Data Handling and Applications? - ?1108.2 Cryptography? - ?301.1 Fluid Flow? - ?731.1.1 Error Handling? - ?941.5 Mechanical Variables Measurements????? - ?941.7 Pressure Measurements? - ?942.1.6 Mechanical and Miscellaneous Measuring Instruments? - ?942.1.7 Special Purpose Instruments? - ?942.1.9 Pressure Measuring Instruments

Numerical data indexing: Percentage 1.40E+01%, Percentage 1.51E+02%, Percentage 1.629E+00%, Percentage 1.74E+02%, Percentage 2.061E+00%, Percentage 2.713E+00%, Percentage 5.15E+02%, Percentage 8.87E+02%, Percentage 9.36E+02%, Percentage 9.90E+02%

DOI: 10.6041/j.issn.1000-1298.2025.02.028

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

8. Path Planning Technical Research of Low-rolling Compaction Harvesting Operation for the First Season of Ratoon Rice

Accession number: 20251017998162

Title of translation: 再生稻头季低碾压收获作业路径规划技术研究

Authors: Hu, Lian (1, 2); Zhang, Hong (1); He, Jie (1, 2); Man, Zhongxian (1); Yue, Mengdong (1); Qu, Gaokai (1); Tang, Qiyuan (3, 4); Huang, Peikui (1, 2); Luo, Xiwen (1, 2)

Author affiliation: (1) Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou; 510642, China; (2) State Key Laboratory of Agricultural Equipment Technology, Guangzhou; 510642, China; (3) Hunan Hongshuo Biotechnology Co., Ltd., Yiyang; 413119, China; (4) College of Agronomy, Hunan Agricultural University, Changsha; 410128, China

Corresponding author: Luo, Xiwen(xwluo@scau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 19-27

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Path planning is one of the key factors determining the efficiency and quality of the first season harvest operation for regenerated rice. At present, there are many studies on the full coverage path planning technology of unmanned agricultural machinery in the operating area, but little consideration is given to the problem of the harvester crushing the regenerated rice during field harvesting. Therefore, research on the path planning of regenerated rice harvesting was conducted with reduced crushing. By analyzing farmland information, waiting areas, and unloading of grain, the problem of harvesting and unloading path planning for regenerated rice was transformed into a vehicle routing problem with capacity constraints (CVRP). A mathematical model for the harvesting path of regenerated rice was constructed with the goal of minimizing the rolling area and minimizing the total path of the harvester. A hybrid algorithm for path planning of regenerated rice with less compaction was proposed, using traditional ant colony algorithm (ACO) and 2 ? opt algorithm to obtain the optimal path. Taking the driverless harvester of ratooning rice as the object, the field experiments of straight path planning, headland turning, grain unloading planning and full link field operation were designed. The field experiments were conducted with the auto drive system to investigate the field compaction rate of the harvester. The results showed that the average absolute error of linear tracking was 3. 51 cm, the maximum deviation was 8. 24 cm, and the compaction rate of the linear segment was 17. 55% . The compaction rate in the head area was decreased by 52. 2% . The path planning designed had a total field compaction rate of 27. 42%, which met the special operational requirements for regenerated rice. The research result can provide theoretical and technical support for reducing the crushing area of unmanned harvesting machines for regenerated rice. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Compaction

Controlled terms: Agricultural robots? - ?Ant colony optimization? - ?Fertilizers? - ?Grain (agricultural product)? - ?Harvesters? - ?Motion planning

Uncontrolled terms: 2 ? opt algorithm? - ?Ant colonies algorithm? - ?Autonomous driving? - ?Compaction rates? - ?Field experiment? - ?Harvest operations? - ?Harvesting operations? - ?Key factors? - ?Regenerative rice? - ?Technical research

Classification code: 1101 Artificial Intelligence? - ?1201.7 Optimization Techniques? - ?1502.1.1.3 Soil Pollution? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals? - ?821.5 Agricultural Products? - ?913.4 Manufacturing

Numerical data indexing: Percentage 2.00E+00%, Percentage 4.20E+01%, Percentage 5.50E+01%, Size 2.40E-01m, Size 5.10E-01m

DOI: 10.6041/j.issn.1000-1298.2025.02.002

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

9. Development of Agricultural Machinery Operation Path Planning Algorithms and Mobile Software for Unmanned Rice Oil Rape Rotation Farms

Accession number: 20251018008469

Title of translation: 稻油轮作无人化农场农机作业路径规划算法与移动端软件研究

Authors: Huang, Xiaomao (1, 2); Wang, Shaoshuai (1); Shi, Yize (1); Huang, Xiya (1); Ma, Yongsheng (1); Luo, Chengming (1, 2)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 73-82

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Unmanned farm will be the ultimate form of rice and oil rape eultivation in the rice - oil rape rotation area in the middle and lower reaches of the Yangtze River in China. By analyzing the production mode, implement type and Operation path planning requirements of unmanned farming in rice and oil rape rotation, intelligent farm implement Operation and maintenance Software for unmanned farming was designed based on the Android application framework, including modules of plot management, implement attribute management and path planning for the path requirements of the field Operation proeess of typical Operation links of unmanned farming for rice and oil rape rotation eultivation. On the basis of the existing algorithms of the group, focusing on designing algorithms for Operation path planning in two typical processes, namely integrated rice harvesting and rapeseed sowing and paddy ploughing, and the field Operation path planning algorithms coded in Python beforehand was called through the mixed programming of Chaquopy plug-in. The results of Simulation test and field test showed that the designed and developed Android Software was stable and reliable, with good human - Computer interaction, and path planning algorithms were able to provide effective Operation paths for different implements and common quadrilateral plots, and the Operation time of the Single Geld Operation path planning algorithm ranged from 29 ms to 1 898 ms, and the eomputational effieiency and rationality of the paths met the needs of unmanned produetion of typieal Operation links in the real application. The eomputational effieiency and path reasonableness met the needs of unmanned produetion in typieal Operations, providing theoretical and technical support for the construetion of unmanned farms in the middle and lower reaches of the Yangtze River for rice and oil rape rotations. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Automatic guidance (agricultural machinery)

Controlled terms: Agricultural implements? - ?Agricultural robots? - ?Crop rotation? - ?Fertilizers? - ?Problem oriented languages

Uncontrolled terms: Android platforms? - ?Automatic navigation? - ?Field operation? - ?Middle and lower reaches of the yangtze rivers? - ?Mobile softwares? - ?Path-planning algorithm? - ?Production modes? - ?Rice - oil rape rotation? - ?Rice oil? - ?Unmanned farm

Classification code: 1106.1.1 Computer Programming Languages? - ?1502.1.1.3 Soil Pollution? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals? - ?821.4 Agricultural Methods

Numerical data indexing: Time 2.90E-02s to 1.00E-03s, Time 8.98E-01s

DOI: 10.6041/j.issn.1000-1298.2025.02.007

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

10. Design and Operational Parameter Optimization of Endurance-extended Drone for Supplementary Pollination in Hybrid Rice Breeding

Accession number: 20251018009076

Title of translation: 航时可延展水稻辅助授粉无人飞机设计与作业参数优选

Authors: Jiang, Rui (1, 2); Lin, Jianqin (1, 3); Lin, Zonghui (1, 4); Liu, Aimin (5); Deng, Konghong (1, 3); Zhou, Zhiyan (1, 6)

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, Guangzhou; 510642, China; (4) Guangdong Engineering Research Center for Agricultural Aviation Application, Guangzhou; 510642, China; (5) Yuan Longping High-tech Agricultural Co., Ltd., Changsha; 410006, China; (6) State Key Laboratory of Agricultural Equipment Technology, Guangzhou; 510642, China

Corresponding author: Zhou, Zhiyan(zyzhou@scau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 229-239

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In recent years, drones have been explored as a potential tool for pollination support in hybrid rice breeding. However, the limited flight endurance of existing electrical agricultural drones necessitates frequent battery replacements, hindering the efficient utilization of the limited effective pollination time window and reducing pollination efficiency. To address the limited endurance of drones in pollination tasks and optimize operational parameters to enhance pollination efficiency, a supplementary pollination drone was designed with extendable flight duration, utilizing a time-sharing parallel power distribution scheme with multiple battery packs, achieving a maximum flight endurance of 50 min. To improve the pollination effectiveness of the prototype, a numerical simulation of the downwash airflow generated by the rotors was conducted by using the Lattice Boltzmann method (LBM). The optimal flight parameters of the prototype were found to be a speed of 4. 5 m/ s and an altitude of 2 m above the male parental canopy. Field experiments were conducted to validate the prototype’s pollination effectiveness and the optimal flight parameters by comparing three drones: the prototype, a quadrotor, and a hexacopter. Data were collected in four dimensions: average pollen grain count per single field of view, fruiting rate, yield, and endurance time for standardized deviation analysis. Results showed that the flight endurance (42 min), the pollination efficiency (10. 5 hm2 per flight), the averaged pollen grain count (6. 98 grains per view, meeting the agronomic requirement of at least 3 grains), the yield (1 996. 5 kg / hm2 ) and the comprehensive score of designed drone were better than two comparison drones. The research result may serve as a reference for enhancing the efficiency of drone supplementary pollination in hybrid rice breeding. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Drones

Controlled terms: Agricultural robots? - ?Battery Pack? - ?Fertilizers? - ?Flight simulators? - ?Grain (agricultural product)

Uncontrolled terms: Design parameters? - ?Endurance time? - ?Flight endurance? - ?Flight parameters? - ?Hybrid rice breedings? - ?Operational parameter optimization? - ?Pollen grains? - ?Potential tool? - ?Supplementary pollination? - ?Wind field simulation

Classification code: 1201.12 Modeling and Simulation? - ?1502.1.1.3 Soil Pollution? - ?652.1.2 Military Aircraft? - ?702.1.2 Secondary Batteries? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals? - ?821.5 Agricultural Products

Numerical data indexing: Mass 5.00E+00kg, Size 2.00E+00m, Time 2.52E+03s, Time 3.00E+03s, Velocity 5.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2025.02.022

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

11. Design and Experiment of Spiral Interlaced Threshing Cylinder for Combine Harvester

Accession number: 20251018009078

Title of translation: 联合收获机螺旋交错排布式脱粒滚筒设计与试验

Authors: Jiang, Tao (1, 2); Li, Haitong (1); Huang, Minghui (2); Zhang, Min (1); Jin, Mei (1); Guan, Zhuohuai (1)

Author affiliation: (1) Nanjing Institute of Agriculture Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing; 210014, China; (2) National Machinery Heavy Industry (Changzhou) Excarator Co., Ltd., Changzhou; 213133, China

Corresponding author: Guan, Zhuohuai(Guan_zh@foxmail.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 314-324

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The threshing cylinder of the crawler-type grain combine harvester tends to become easily clogged under high feeding rate conditions, which leads to severe fragmentation of threshed materials, resulting in an accumulation of residuals on the cleaning screen, which affects grain sieving efficiency, increases cleaning losses, and significantly elevates power consumption. By optimizing the structure and arrangement of the rasp bars and threshing elements, a spiral interlaced arrangement threshing cylinder was designed. This design reduced fluctuations in cylinder load and decreased the impact intensity of the threshing elements on the material, thereby minimizing material breakage and fragmentation. Through discrete element simulation and bench tests, the distribution patterns of threshed grain were analyzed. A comparative analysis was conducted on the migration speed of stalks within the original cylinder and the designed cylinder, as well as the torque exerted on the cylinder. The simulation results indicated that at rotational speed of 750 r / min, the migration speed of stalks within the designed cylinder was increased by 23. 5%, facilitating the rearward conveyance of materials. Additionally, the average torque of the designed cylinder was decreased by 29. 2%, with reduced torque fluctuations, thereby improving the stability of the threshing operation. Field comparison test results showed that the designed cylinder significantly reduced stalk breakage and fragmentation, thereby decreasing the load on the cleaning screen and increasing the probability of grain passing through the vibrating screen. The cleaning loss rate was reduced by an average of 22. 8% . Moreover, under the same operating conditions, the combine harvester equipped with the designed cylinder demonstrated an average fuel consumption reduction of 18. 1% . Combining the results of the simulation experiments and field comparison tests, it was evident that the designed threshing cylinder can reduce operational losses, enhance working efficiency, and lower threshing power consumption. It effectively met the requirements for low-loss, high-efficiency operation under high feeding rate conditions. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 21

Main heading: Vibrating screens

Controlled terms: Cleaning? - ?Grain (agricultural product)

Uncontrolled terms: Cleaning loss? - ?Combine harvesters? - ?Comparison test? - ?Condition? - ?Crawler types? - ?Feeding rate? - ?Grain combines? - ?Power? - ?Spiral interlaced? - ?Threshing cylinder

Classification code: 605.1 Small Tools, Powered? - ?802.3 Chemical Operations? - ?821.5 Agricultural Products

Numerical data indexing: Angular velocity 1.2525E+01rad/s, Percentage 1.00E00%, Percentage 2.00E+00%, Percentage 5.00E+00%, Percentage 8.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.02.029

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

12. Field Road Extraction Method Based on Improved U Net for High-resolution Orthophoto Maps

Accession number: 20251018008479

Title of translation: 基于改进U-Net的高分辨率正射影像图田间可行驶道路提取方法

Authors: Jin, Zhiwen (1); Wang, Ning (1); Xiao, Jianxing (1); Wang, Tianhai (1); Qiu, Ruicheng (1); Li, Han (1); Zhang, Man (1)

Author affiliation: (1) Key Laboratory qf Smart Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing; 100083, China

Corresponding author: Zhang, Man(cauzm@cau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 155-163

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The aoquisition of field road boundary information is the basis for making high-preeision farmland map. In order to solve the problems of inaeeurate segmentation of field roads in high-resolution orthophoto maps, such as missed segmentation and false segmentation, a deep learning network model was proposed based on improved U - Net. Firstly, the baekbone network was replaced with ResNet50 to enhanee the ability to extract the features of drivable roads in the field. Secondly, the DSConv module, which can improve the aecuraey of tubul?r strueture, improved the accuracy of the field drivable road, and inhibited the feature extraction of the background of field features similar to the field road. Finally, the complete context information was obtained by inserting the ECA - Net attention mechanism, and the feature restoration process of the drivable road in the field was optimized, so as to improve the Overall segmentation accuracy of the model. Then the traditional image processing method was used to further denoise and eliminate the hole of the segmentation results, and in view of the problem of losing geographic information in the recognition results, so as to obtain high-precision field road boundary information. The experimental results showed that the improved U _ Net model had the highest evaluation index values in the comparison with the semantic segmentation model in the test set of the constructed dataset with 95.46% MPA and 91. 12% MIoU, after post-prooessing using traditional image processing methods, MIoU and MPA were 92. 64% and 96. 75%, respectively, the MIoU and MPA were increased by 1. 29 percentage points and 1. 52 percentage points; and 86. 39% and 90. 01% in the field drivable road recognition test of high-resolution orthophoto map, respectively, which can clearly identify the field road. After using the traditional image processing method to optimize the obtained high-resolution orthophoto results, the MIoU and MPA were 88.34% and 91.53%, respectively, and the MIoU and MPA were increased by 1. 95 percentage points and 1. 52 percentage points, respectively. The research result can provide accurate field road boundary information for the subsequent production of high-precision farmland map. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Semantic Segmentation

Controlled terms: Agricultural robots? - ?Gluing? - ?Image denoising? - ?Thermal spraying

Uncontrolled terms: Boundary information? - ?Deep learning? - ?Field road extraction? - ?High resolution? - ?High-precision? - ?Image processing - methods? - ?Orthophoto maps? - ?Percentage points? - ?Road extraction? - ?Semantic segmentation

Classification code: 1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?208.1 Coating Techniques? - ?210 Adhesive Materials? - ?214 Materials Science? - ?716.1 Information Theory and Signal Processing? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Percentage 1.00E00%, Percentage 1.20E+01%, Percentage 3.90E+01%, Percentage 6.40E+01%, Percentage 7.50E+01%, Percentage 8.834E+01%, Percentage 9.153E+01%, Percentage 9.546E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.015

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

13. Lightweight Detection Method for Seeding Quality of Chili Seedling Trays Based on Improved YOLO v8n

Accession number: 20251018008446

Title of translation: 基于改进YOLO v8n的辣椒穴盘育苗播种质量轻量级检测方法

Authors: Kong, Dehang (1, 2); Liu, Yunqiang (1, 2); Cui, Wei (1, 2); Wu, Haihua (1, 2); Zhang, Xuedong (1, 2); Ning, Yichao (1, 2)

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

Corresponding author: Wu, Haihua(caamswhh@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 381-392

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of real-time and accurate detection of the seeding quality of chili seedling trays, considering the Computing power limitations of edge devices, a lightweight detection algorithm YOLO v8n -SCS (YOLO v8n improved with StarNet, CAM, and SCConv) was proposed based on YOLO v8n. Meanwhile, the technical strategy of “single-cell training + whole-tray detection” was adopted to reduce training costs and improve training efficiency. Firstly, the StarNet lightweight network and the CAM (Context augmentation module) were used as the backbone network to achieve multi-receptive field Information fusion of deep features while reducing the complexity of the model. Secondly, the spatial and channel reconstruction convolution (SCConv) was employed to optimize the bottleneck structure of the intermediate layer cross stage partial network fusion (C2f) module to enhance the feature extraction ability of the module and improve the model inference speed. Finally, the P2 detection layer was fused and the detection heads were reduced to one to enhance the model’ s detection Performance for small targets. The results showed that the YOLO v8n - SCS model had a parameter quantity of 1. 2 X 10, a memory ocoupation of 2. 7 MB, and a computation amount of 7. 6 x 10 FLOPs. On the single-eell dataset of the seedling trays, its mAP50 was 98. 3%, mAP50_95 was 83. 8%, and the frame rate was 112 f/s. Compared with the baseline model YOLO v8n, the parameter quantity was reduced by 62. 5%, mAP50 was increased by 2. 5 pereentage points, mAP50_95 was increased by 2. 1 pereentage points, the floating-point Operations were redueed by 1. 3 x 10, and the frame rate was increased by 23. 1%. In the whole-tray detection task, its deteetion frame rate was 21 f/s and the detection accuraey rate was 98.2%. Compared with the baseline model, the detection frame rate was increased by 8.2% and the accuraey rate was increased by 1. 1 pereentage points. For 72-cell seedling trays with a seeding speed within 800 trays/h and 128-cell seedling trays with a seeding speed within 600 trays/h, its average detection accuraey was above 96%, and the detection errors of single-seed rate, reseeding rate, and miss-seeding rate were less than 1. 8%. This study achieved a good balance between Performance and computational cost, reduced the Computing power requirements for deploying edge devices, met the online detection needs for the seeding quality of chili seedling trays, and provided key technical support for the intelligent Upgrade of the seedling produetion line. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Cams

Controlled terms: Digital storage? - ?Digital to analog conversion? - ?Edge detection? - ?Image segmentation? - ?Information fusion? - ?Program debugging

Uncontrolled terms: Baseline models? - ?Chili seed? - ?Computing power? - ?Frame-rate? - ?Improved YOLO v8n? - ?Lightweight model? - ?Plug seedling? - ?Quality detection? - ?Seeding qualities? - ?Seeding quality detection

Classification code: 1103.1 Data Storage, Equipment and Techniques? - ?1106.1 Computer Programming? - ?1106.2 Data Handling and Data Processing? - ?1106.3 Digital Signal Processing? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?601.2 Machine Components? - ?601.3 Mechanisms? - ?903.1 Information Sources and Analysis

Numerical data indexing: Percentage 1.00E00%, Percentage 3.00E+00%, Percentage 5.00E+00%, Percentage 8.00E+00%, Percentage 8.20E+00%, Percentage 9.60E+01%, Percentage 9.82E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.035

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

14. Control System of Mud Collection Device for Rice Seedling Cultivation Machines

Accession number: 20251018008462

Title of translation: 田间水稻育秧机取泥装置控制系统研究

Authors: Kuang, Fuming (1, 2); Ji, Ao (1, 2); He, Jing (3); Wang, Jun (4); Xiong, Wei (1, 2); Zhu, Dequan (1, 2); Zheng, Quan (1)

Author affiliation: (1) School of Engineering, Anhui Agricultural University, Hefei; 230036, China; (2) Anhui Provincial Engineering Laboratory for Intelligent Agricultural Machinery Equipment, Hefei; 230036, China; (3) Chinese Academy of Agricultural Mechanizatwn Seiences Group Co., Ltd., Beiing; 100083, China; (4) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou; 310058, China

Corresponding author: Zhu, Dequan(zhudequan@ahau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 290-304

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the fragmented and small-scale fields of southern hilly regions, field-based mud collection is crucial for rice seedling cultivation. While mechanized mud collection can reduce costs, save time and labor, and improve seedling adaptability, challenges such as the hard underlying layer and uneven mud surface in paddy fields complicate dynamic adjustment of operational parameters. This results in unstable mud collection quantities and difficulties in achieving continuous field-based mud collection and seedling cultivation. A control System model for mud collection was developed by using Matlab/Simulink, targeting mud mass as the control objeetive, with mud collection depth and spiral conveyor speed as controlled variables, employing a fuzzy PID control approach. Simulation results showed that at a forward speed of 0. 15 m/s, the mud mass in the storage tank was stabilized within 18 s, with an overshoot of 0. 125%. To validate the system’s Performance, a test rig equipped with displacement, pressure, and tilt sensors, and a PLC1200 Controller was construeted. Experimental results indicated that the maximum Standard deviation of mud mass was 0. 46 kg, with a minimum of 0. 21 kg; the maximum and minimum mud masses in the storage tank were 8. 8 kg and 7. 1 kg, respectively. The System effectively met Geld operational requirements, with no mud aecumulation observed at the front end of the spiral conveyor. The successful implementation of this control System enabled more efficient and simplified rice cultivation.This System can provide a promising Solution for improving agricultural produotivity in fragmented and challenging terrains. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Three term control systems

Controlled terms: Agricultural robots? - ?Conveyors? - ?Seed? - ?Tanks (containers)

Uncontrolled terms: Collection device? - ?Field taking mud rice seeding machine? - ?Fuzzy-PID control? - ?Mud storage tank mass? - ?PLC control? - ?Reduce costs? - ?Rice seedlings? - ?Small scale? - ?Spiral conveyor submersion depth monitoring? - ?Storage tank

Classification code: 610.2 Tanks and Accessories? - ?692.1 Conveyors? - ?731.1 Control Systems? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products

Numerical data indexing: Mass 1.00E00kg, Mass 2.10E+01kg, Mass 4.60E+01kg, Mass 8.00E+00kg, Percentage 1.25E+02%, Time 1.80E+01s, Velocity 1.50E+01m/s

DOI: 10.6041/j.issn.1000-1298.2025.02.027

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

15. Prediction of RUL of Harmonie Reducer Based on MDBO CNN LSTM Method

Accession number: 20251018008453

Title of translation: 谐波减速器MDBO-CNN-LSTM剩余使用寿命预测

Authors: Lan, Yuezheng (1, 2); Liu, Biao (1, 2); Shi, Chao (1, 2); Guo, Shijie (1, 2); Lu, He (1, 2); Tang, Shufeng (1, 2)

Author affiliation: (1) College of Mechanical Engineering, Inner Mongolin University of Technology, Hohhot; 010051, China; (2) Inner Mongolin Key Lahoratory of Roboties and Intelligent Equipment Technology, Hohhot; 010051, China

Corresponding author: Guo, Shijie(zijianguoxjlu2015@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 533-543

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address challenges in predicting the remaining useful life (RUL) of harmonio drives-such as difficulties in selecting degradation nodes, poor physical interpretability of degradation indicators, and large prediction deviations, a novel approach was proposed. The method combined a one-dimensional stacked convolutional autoeneoder (SCAE) integrated with deep eonvolutional embedded clustering (DCEC) for degradation point extraetion, along with an improved düng beetle optimization (DBO) algorithm to enhance the Performance of a CNN _ LSTM-based RUL prediction model. The Vibration signals were processed by using the SCAE - DCEC framework to identify degradation nodes, addressing issues related to the difficulty of node selection and the low compatibility between degradation indicators and the predictive network. Secondly, a modified düng beetle optimization (MDBO) algorithm was developed, incorporating SPM chaotic mapping, adaptive probability thresholds, and differential mutation perturbations, with its Performance rigorously evaluated. Thirdly, the MDBO algorithm was applied to optimize the hyperparameters of the CNN — LSTM model, forming the MDBO - CNN - LSTM RUL prediction model. An accelerated life test and Validation experiment were condueted by using a harmonic drive test bench. The experimental results demonstrated that the MDBO - CNN - LSTM model significantly outperformed CNN, LSTM, CNN - LSTM, DBO - CNN - LSTM, fully convolutional networks, and Bayesian-optimized LSTM models in terms of goodness of fit. The proposed model achieved a predietion acouracy of 91.33% and exhibited superior reoognition capability for oapturing the degradation trends during the late stages of the lifeeycle of harmonie drive. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Convolution

Controlled terms: Linear regression? - ?Polynomial regression

Uncontrolled terms: DCEC? - ?Dung beetles? - ?Harmonie reducer? - ?LSTM? - ?Modified dung beetle optimization? - ?Optimisations? - ?Optimization algorithms? - ?Point of degradation? - ?Remaining useful lives? - ?Stacked convolutional autoeneoder

Classification code: 1202.2 Mathematical Statistics? - ?716.1 Information Theory and Signal Processing

Numerical data indexing: Percentage 9.133E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.050

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

16. Fishing Effort Estimation of Trawlers Based on Vessel Monitoring System Data

Accession number: 20251018008499

Title of translation: 基于船舶监测系统数据的拖网渔船捕捞努力量估算

Authors: Li, Dan (1); Lu, Feng (1, 2); Xu, Shuo (1, 2); Wang, Yu (1, 2); Xue, Muhan (1); Ni, Hanchen (1); Fang, Hui (2, 3); Zhang, Man (4); Ma, Zhenhua (5); Chen, Zuozhi (5); Xu, Jian (1)

Author affiliation: (1) Institute of Fisheries Engineering, Chinese Academy of Fishery Sciences, Beijing; 100141, China; (2) L(M)shan Laboratory, Qingdao; 266231, China; (3) East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai; 200090, China; (4) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (5) South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou; 510300, China

Corresponding author: Xu, Shuo(xush@cafs.ac.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 523-532

Language: English

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Estimating trawler fishing effort plays a critical role in characterizing marine fisheries aetivities, quantifying the eeological impaet of trawling, and refining regulatory frameworks and polieies. Understanding trawler fishing inputs offers crueial scientific data to support the sustainable management of offshore fishery resources in China. An XGBoost algorithm was introduced and optimized through Harris Hawks Optimization (HHO), to develop a model for identifying trawler fishing behaviour. The model demonstrated exceptional Performance, achieving accuracy, sensitivity, specificity, and the Matthews correlation coefficient of 0. 971 3, 0. 980 6, 0. 963 2, and 0. 942 5, respectively. Using this model to detect fishing aetivities, the fishing effort of trawlers from Shandong Province in the sea area between 119°E to 124°E and 32°N to 40°N in 2021 was quantified. A heatmap depicting fishing effort, generated with a spatial resolution of 1/8°, revealed that fishing aetivities were predominantly concentrated in two regions: 121. 1°E to 124°E, 35. 7°N to 38. 7°N, and 119. 8°E to 122. 8°E, 33.6°N to 35.4°N. This research can provide a foundation for quantitative evaluations of fishery resources, which can offer vital data to promote the sustainable development of marine capture fisheries. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Fisheries

Controlled terms: Fishing (oil wells)

Uncontrolled terms: Effort Estimation? - ?Fishery resources? - ?Fishing effort? - ?Machine-learning? - ?Marine Fishery? - ?Position data? - ?Regulatory frameworks? - ?Trawler? - ?Vessel monitoring systems? - ?Vessel position data

Classification code: 471.5 Sea as Source of Minerals and Food? - ?512.1.2 Petroleum Development Operations? - ?822 Food Technology

DOI: 10.6041/j.issn.1000-1298.2025.02.049

Funding text: Foundation items: Central Public-interest Scientific Institution Basal Research Fund (2024HY - ZC006 and 2023TD91); the National Key Research and Development Program of China (2024YFD2400505 and 2024YFD2400801); Laoshan Laboratory (LSKJ202203003)

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

17. Classification of Cotton Verticillium wilt Severity Levels Based on Transformer FNN and UAV Hyperspectral Remote Sensing Technology

Accession number: 20251018008440

Title of translation: 基于Transformer FNN和无人机高光谱遥感技术的棉花黄萎病危害等级分类研究

Authors: Liao, Juan (1, 2); Liang, Yexiong (1, 2); Jiang, Rui (1, 2); Xing, He (3, 4); He, Xinying (1, 2); Wang, Hui (1, 2); Zeng, Haoqiu (1, 2); He, Songwei (1, 2); Tang, Saiou (1, 2); Luo, Xiwen (1, 5)

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) State Key Laboratory of Agricultural Equipment Technology, Guangzhou; 510642, China; (4) School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou; 511363, China; (5) Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL—AAI), Guangzhou; 510642, China

Corresponding author: Luo, Xiwen(xwluo@scau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 240-251

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of high spectral data redundancy and the limited accuracy of traditional machine learning models in identifying cotton Verticillium wilt severity levels, Nano - Hyperspeo hyperspeotral oameras were mounted on drones to collect hyperspectral images of cotton fields. The spectral response characteristics of cotton canopies to different severity levels of Verticillium wilt were analyzed. An optimal Vegetation index combination was identified and used to establish a monitoring model suitable for severity Classification. This approach enabled precise Classification of Verticillium wilt severity levels. The minimum redundancy maximum relevance algorithm was applied to rank the importance of features among 17 Vegetation indices and 270 spectral bands. Features selected by this algorithm were incrementally grouped and input into an eXtreme gradient boosting model. This process determined the Vegetation indices and spectral bands most strongly correlated with Verticillium wilt severity levels. A Transformer - FNN (feedforward neural network) Classification model was then developed. Vegetation indices and spectral features were used as inputs to this model for Classification. The Classification accuracy of Vegetation indices and spectral features in identifying Verticillium wilt severity levels was compared. Additionally, Classification models based on back propagation neural network (BPNN), Transformer, and support vector machines (SVM) were constructed. The accuracy of these models was validated and analyzed. The results showed that the optimal Vegetation index combination for Verticillium wilt severity Classification was MSR and TVI. The optimal spectral band combination included 430 nm, 439 nm, 488 nm, 566 nm, 697 nm, 722 nm, 742 nm, 764 nm, 769 nm, 782 nm, 822 nm, 831 nm, 858 nm, 873 nm, 878 nm, 893 nm, 909 nm, and 985 nm. Using the Transformer - FNN model, the overall Classification accuracy based on Vegetation indices reached 95. 6% . This represented a 6. 2 percentage points improvement compared with the accuracy achieved by using spectral features, which was 89. 4%. For Vegetation indices, the Transformer - FNN model achieved a Classification accuracy of 95. 6%. This was 11. 2 percentage points higher than the accuracy of the BPNN model, 17. 2 percentage points higher than that of the Transformer model, and 30. 8 percentage points higher than that of the SVM model. The research proposed a high-accuracy monitoring method for cotton Verticillium wilt based on Vegetation indices. It provided an effective approach for large-scale and precise monitoring of cotton Verticillium wilt. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Cotton

Controlled terms: Adaptive boosting? - ?Agricultural robots? - ?Feedforward neural networks? - ?Photomapping? - ?Redundancy? - ?Support vector machines

Uncontrolled terms: Cotton verticillium wilt? - ?Feature combination? - ?Feed forward? - ?Hyperspectral remote sensing? - ?MRMR? - ?Neural-networks? - ?Transformer? - ?Vegetation index? - ?Verticillium wilt? - ?Xgboost

Classification code: 1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1106 Computer Software, Data Handling and Applications? - ?405.3 Surveying? - ?731.6 Robot Applications? - ?742.1 Photography? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?913.3 Quality Assurance and Control

Numerical data indexing: Percentage 4.00E+00%, Percentage 6.00E+00%, Size 4.30E-07m, Size 4.39E-07m, Size 4.88E-07m, Size 5.66E-07m, Size 6.97E-07m, Size 7.22E-07m, Size 7.42E-07m, Size 7.64E-07m, Size 7.69E-07m, Size 7.82E-07m, Size 8.22E-07m, Size 8.31E-07m, Size 8.58E-07m, Size 8.73E-07m, Size 8.78E-07m, Size 8.93E-07m, Size 9.09E-07m, Size 9.85E-07m

DOI: 10.6041/j.issn.1000-1298.2025.02.023

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

18. Path Tracking Control Algorithm for Tracked Agricultural Chassis Based on Parameter-pre-tuned Super-twisting Sliding Mode Control

Accession number: 20251018009077

Title of translation: 基于参数预调型超螺旋滑模控制的履带农用底盘路径跟踪算法研究

Authors: Liu, Huanyu (1); Zou, Shun (1); Tang, Jiacheng (1); Han, Zhihang (1); Yu, Hao (1); Wang, Shuang (1)

Author affiliation: (1) Institute of Modern Agricultural Equipment, Xihua University, Chengdu; 610039, China

Corresponding author: Wang, Shuang(wsh@mail.xhu.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 136-144

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: To address the challenges of high-frequency oscillations and difficulty in parameter tuning typically encountered in traditional sliding mode control (SC) for path tracking in agricultural machinery, a novel control approach was proposed. The research focused on a tracked agricultural chassis, where a kinematic model and deviation dynamic equations were firstly developed. A super-twisting sliding mode control (TSC) law was then designed to ensure accurate path tracking. In addition, a parameter pre-tuning controller was introduced to facilitate the fine-tuning of control parameters. The finite memory Broyden algorithm was employed to optimize the parameters of the super-twisting sliding mode controller, enhancing its robustness. The stability of the path tracking system was verified by using Lyapunov stability analysis, ensuring that the proposed control method maintained stability under various operational conditions. Simulation results demonstrated that when the tracked agricultural chassis operated at a speed of 1. 0 m/s, the maximum absolute tracking error was reduced to 0. 063 m, and the average absolute error was minimized to 0. 013 m. When compared with traditional linear PID control and conventional sliding mode control, the maximum deviation was reduced by 61. 3% and 62. 1%, respectively. Furthermore, the absolute average error was decreased by 89. 2% and 75. 4%, respectively. These results indicated a significant improvement in tracking accuracy with the proposed method. Field test results further validated the effectiveness of the control algorithm. When the operational speeds were 0. 5 m/s and 1. 0 m/s, the absolute average error of the parameter pre-tuned super-twisting sliding mode control algorithm was reduced by 69. 2% and 50%, respectively, compared with that of the traditional sliding mode control. Additionally, the heading error was reduced by 61. 1% and 40%, respectively, contributing to a noticeable reduction in high-frequency oscillations during operation. Overall, the proposed TSC strategy significantly enhanced the path tracking performance of agricultural machinery, providing a more stable and reliable control method compared with existing approaches. The method not only improved tracking accuracy but also effectively mitigated high-frequency oscillations, making it suitable for real-world applications in agricultural vehicle guidance systems. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Three term control systems

Controlled terms: Agricultural robots? - ?Fertilizers? - ?Invariance? - ?Robustness (control systems)

Uncontrolled terms: Average errors? - ?Control methods? - ?High frequency oscillations? - ?Path tracking? - ?Pre-tuning controller? - ?Sliding-mode control? - ?Super- twisting? - ?Super-twisting sliding mode control? - ?Tracked agricultural chassi? - ?Tuning controllers

Classification code: 1502.1.1.3 Soil Pollution? - ?731.1 Control Systems? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals

Numerical data indexing: Percentage 1.00E00%, Percentage 2.00E+00%, Percentage 3.00E+00%, Percentage 4.00E+00%, Percentage 4.00E+01%, Percentage 5.00E+01%, Size 1.30E+01m, Size 6.30E+01m, Velocity 0.00E00m/s, Velocity 5.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2025.02.013

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

19. Path Planning of Robot Based on Improved Sparrow Search Algorithm and Bessel Curve

Accession number: 20251018009045

Title of translation: 基于改进麻雀搜索算法和贝塞尔曲线的无人农场机器人路径规划方法

Authors: Lu, Jianqiang (1, 2); Chen, Zucheng (2); Lan, Yubin (2, 3); Tong, Haiyang (2); Bao, Guoqing (2); Zhou, Zhengyang (2); Zheng, Jiaqi (2)

Author affiliation: (1) State Key Laboratory of Agricultural Equipment Technology, Guangzhou; 510642, China; (2) College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; 510642, China; (3) National Center for International Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou; 510642, China

Corresponding author: Lan, Yubin(ylan@scau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 115-123

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Optimizing unmanned farm paths to improve farm management efficiency and resource utilization is a hot research topic in the field of mobile robot navigation. An improved sparrow search algorithm (ISSA) incorporating improved Q-learning (IQL) algorithm was designed to address the problems of low search efficiency and smooth paths that can easily fall into local optimization of traditional sparrow search algorithm (SSA) and reinforcement learning algorithm. ISSA incorporating the improved IQL algorithm was designed for global path planning of mobile robots in combination with Bessel curves. Firstly, a multi-strategy initialization of the population was used at the beginning of the algorithm, combining the IQL algorithm with Logistic chaos mapping and Latin hypercube sampling (LHS) methods to provide excellent and diverse initial solutions for the population; secondly, a linear dynamic inertia weight adjustment method was introduced into the finder position updating to balance the algorithm’s global search capability and local exploitation capability, and improve the convergence speed of the algorithm; then, the reverse learning strategy was introduced into the vigilant to further explore the unexplored area and prevent falling into the local optimal solution; finally, the path was smoothed by combining obstacle avoidance algorithms and Bessel curves to eliminate the problems of traveling paths too close to obstacles and unsmooth paths. The effectiveness and superiority of ISSA algorithm was verified through comparative simulation tests on Matlab platform. The experimental results showed that the ISSA algorithm effectively combined the self-learning characteristics of the IQL algorithm and the powerful search capability of the SSA algorithm, which significantly improved the efficiency of global path optimization and generated smoother paths in both the grid simulation environment and the field scenario. In the field scenario, the ISSA algorithm reduced the path planning time by 64. 43% and 9. 94%, and the average value of the shortest path length by 8. 3% and 12%, respectively, compared with the SSA and ACO algorithms, which provided a high-quality path planning solution for the unmanned farm robots to work accurately and efficiently. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Mobile robots

Controlled terms: Adversarial machine learning? - ?Agricultural robots? - ?Bessel functions? - ?Fertilizers? - ?Motion planning? - ?Reinforcement learning? - ?Robot learning? - ?Robot programming

Uncontrolled terms: Bessel curve? - ?Farm management? - ?Field scenarios? - ?Hot research topics? - ?Improved sparrow search algorithm? - ?Management efficiency? - ?Q-learning algorithms? - ?Resources utilizations? - ?Search Algorithms? - ?Unmanned farm

Classification code: 1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1106.1 Computer Programming? - ?1201.2 Calculus and Analysis? - ?1502.1.1.3 Soil Pollution? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals

Numerical data indexing: Percentage 1.20E+01%, Percentage 3.00E+00%, Percentage 4.30E+01%, Percentage 9.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.011

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

20. Research Status and Outlook of Farmland Boundary Recognition Technology in Large-scale Unmanned Smart Farms

Accession number: 20251018008488

Title of translation: 大田无人化智慧农场农田边界识别技术研究现状与展望

Authors: Luo, Xiwen (1, 2); Gu, Xiuyan (1, 2); Hu, Lian (1, 2); Zhao, Runmao (1, 2); Yue, Mengdong (1, 2); He, Jie (1, 2); Huang, Peikui (1, 2); Wang, Pei (1, 2)

Author affiliation: (1) Key Lahoratory of Key Technology on Agricultural Machine and Equipment, Ministry of Ediwation, South China Agricultural University, Guangzhou; 510642, China; (2) State Key Lahoratory of Agricultural Equipment Technology, 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: 2

Issue date: February 2025

Publication year: 2025

Pages: 1-18

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Smart agrioulture is the development direotion of modern agriculture, and unmanned smart farms are an important way to achieve smart agrioulture. Unmanned smart farm is an important direction for agrioultural transformation and upgrading, and its precise and efficient Operation quality depends on the accuracy and reliability of farmland boundary recognition technology. The technical System and Workflow methods of farmland boundary recognition were systematically reviewed, with a focus on analyzing the characteristics and application scenarios of three types of data acquisition methods: satellite remote sensing, UAV remote sensing, and ground-based sensing. The advantage of satellite remote sensing lies in its wide-area, periodic monitoring capability that Supports large-scale farmland change analysis, though its spatial resolution is limited. UAV high-resolution images, when deeply integrated with ground sensors (LiDAR point clouds and RGB image registration), can achieve centimeter-level boundary segmentation, providing high-precision data support for complex farmland scenarios, but its field of view is limited. Traditional image processing algorithms (threshold segmentation, edge detection) offer real-time advantages in regul?r farmlands but struggle with scenarios involving objects of similar speotra and static element ooclusions. Deep learning-based models such as U - Net and DeepLab, through multi-seale feature fusion and attention mechanisms, significantly enhanee the robustness of irregul?r boundary recognition. Current technologies support the construetion of digital maps and agrieultural maehinery path planning. However, there are still three main bottleneeks: insufficient spatio-temporal alignment aecuraey of multi-source data, resulting in low fusion efficiency; slow inference speeds of lightweight models on edge Computing deviees, which failed to meet real-time Operation demands; and the laek of dynamie farmland boundary update meehanisms, restrieting long-term monitoring effectiveness. Future research should focus on multi-modal spatio-temporal feature fusion, lightweight model technologies driven by edge inference, and a framework for autonomous updating of digital farmland maps supported by air - space - ground collaboration, to provide theoretical support for high-precision, high -response, and high-dynamic boundary recognition in farmlands. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 127

Main heading: Bottles

Controlled terms: Agricultural robots? - ?Conformal mapping? - ?Geological surveys? - ?Image registration? - ?Motion planning? - ?Network security? - ?Photointerpretation? - ?Sensor data fusion? - ?Steganography

Uncontrolled terms: Agrieultural road? - ?Boundary recognition? - ?Field ridge? - ?Ground? - ?High-precision? - ?Large-scales? - ?Planting areas? - ?Satellite remote sensing? - ?Smart farm? - ?Space

Classification code: 1101 Artificial Intelligence? - ?1106 Computer Software, Data Handling and Applications? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing? - ?1108.2 Cryptography? - ?1201.14 Geometry and Topology? - ?1201.2 Calculus and Analysis? - ?405.3 Surveying? - ?481.1 Geology? - ?694.1 Packaging Materials and Equipment? - ?731.6 Robot Applications? - ?742.1 Photography? - ?821.2 Agricultural Machinery and Equipment

DOI: 10.6041/j.issn.1000-1298.2025.02.001

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

21. Synchronous Optimization and Optimal Design Criteria of Random Water Supply Micro-irrigation Network System

Accession number: 20251018008441

Title of translation: 随机供水微灌管网系统同步优化与最优设计准则研究

Authors: Ma, Penghui (1, 2); Song, Changji (1); Jing, Ming (1); Hu, Yajin (3, 4); Liang, Bingjie (1, 2); Song, Jingru (1, 3); Fang, Mingyuan (1, 3); Zhang, Huimin (1)

Author affiliation: (1) Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Zhengzhou; 450003, China; (2) Henan Engineering Research Center of Rural Water Environment Improvement, Zhengzhou; 450003, China; (3) Henan Key Laboratory of Ecological Environment Protection and Restoration of the Yellow River Basin, Zhengzhou; 450003, China; (4) College of Resources and Environment, Henan Agricultural University, Zhengzhou; 450046, China

Corresponding author: Zhang, Huimin(zhmxx168@126.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 444-453

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: When the irrigation area uses surface water as its water source and has sufficient supply, the optimization of the micro-irrigation pipe network requires determining the reasonable eontrol area and layout form of individual miero-irrigation Systems and condueting optimized design. The current pipe network optimization method is not applicable to the optimization of random water supply micro-irrigation pipe network Systems under this Situation. In order to achieve the synchronization optimization of layout and pipe diameter for random water supply micro-irrigation pipe network Systems without limiting the area, and propose the optimal design criteria, a mathematical model for optimizing micro-irrigation main pipe network was established, and a model Solution method based on a hybrid encoding genetic algorithm was provided. The correlation between the annual cost per unit area of the main pipe network and its layout was analyzed and the influence of lateral diameter, emitter design discharge, emitter spacing, and emitter emission exponent on the unit area annual cost of main pipe network and micro-irrigation pipe network System was analyzed. The results of example calculations showed that the unit area annual eost of the micro-irrigation pipe network System optimized based on the model with maximum control area for lateral double-sided layout was the lowest, while the unit area annual cost of the micro-irrigation pipe network System optimized based on the model with minimum unit area annual cost for lateral one-sided layout was the highest. Compared with the model with the minimum unit area annual cost for lateral one-sided layout, the percentage reduction in unit area annual cost of the micro-irrigation pipe network System optimized based on the model with maximum control area for lateral double-sided layout was 4. 46% to 15.74%. In actual engineering projects, using smaller lateral diameters, emitter design discharges, emitter emission exponents, larger spacing between emitters, optimizing subunit based on the model with maximum control area for lateral double-sided layout, can effectively reduce costs. The results can provide a basis for the optimization design of random water supply micro-irrigation pipe network Systems without limiting the area. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 20

Main heading: Irrigation

Controlled terms: Behavioral research? - ?Cost engineering? - ?Design for testability? - ?Engineering research? - ?Error correction? - ?Industrial research? - ?Machine design

Uncontrolled terms: Design criteria? - ?Irrigation Networks? - ?Irrigation pipes? - ?Micro-irrigation network system? - ?Microirrigation? - ?Network systems? - ?Optimisations? - ?Pipe network systems? - ?Random water supply? - ?Synchronous optimization

Classification code: 101.5 Ergonomics and Human Factors Engineering? - ?601 Mechanical Design? - ?731.1.1 Error Handling? - ?821.4 Agricultural Methods? - ?901.3 Engineering Research? - ?904 Design? - ?911 Cost and Value Engineering; Industrial Economics? - ?912.1 Industrial Engineering? - ?971 Social Sciences

Numerical data indexing: Percentage 4.60E+01% to 1.574E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.041

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

22. Effects of Combined Application of Organic and Inorganic Fertilizer on Greenhouse Gas Emission and Nitrogen Use Efficiency of Winter Wheat Farmland

Accession number: 20251018009032

Title of translation: 有机无机肥配施对冬小麦农田温室气体排放与氮肥利用效率的影响

Authors: Ma, Penghui (1, 2); Song, Changji (1); Song, Jingru (1); Chen, Weiwei (1); Yang, Jian (1, 2); Fang, Mingyuan (1, 3); Wu, Yulei (1, 3); Hu, Yajin (3, 4)

Corresponding author: Hu, Yajin(huyajinxn@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 474-484

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to explore the fertilization mode of emission reduction, superior yield and high efficiency, taking winter wheat (Xiaoyan 22) as the research object, the experiment set six treatments: no fertilization (KB), conventional fertilization (NP), and organic fertilizer substituting 25% (25% OF),50% (50% OF), 75% (75% OF), and 100% (100% OF) of inorganic fertilizer on equal nitrogen basis, was used to analyze the effects of different proportions of combined organic and inorganic fertilizer application on greenhouse gas emissions, yield, and nitrogen use efficiency in winter wheat farmland, put forward the combined organic-inorganic fertilization mode with the goal of emission reduction, superior yield and high efficiency. The results showed that in the two winter wheat growing seasons, the average cumulative CO2 emissions of 75% OF and 25% OF were the lowest, which was decreased by 7. 62% and 15. 31% compared with that of NP, respectively. The CO2 emission flux was increased in summer and decreased in winter. Among all fertilization treatments, NP had the highest CH4 absorption, while 100% OF had the lowest CH4 absorption. The absorption of CH4 in soil was decreased with the increase of the amount of organic fertilizer. The cumulative N2 O emissions of 75% OF and 100% OF were the lowest, which was decreased by 92. 94% and 159. 47% compared with that of NP, respectively. The global warming potential (GWP) and greenhouse gas intensity (GHGI) of 75% OF were the lowest, which was decreased by 27. 19% and 41. 38% compared with NP, respectively. Compared with NP, the winter wheat yield of 50% OF and 75% OF was increased by 15. 78% ~ 17. 73% and 18. 64% ~ 23. 07%, respectively, from 2018 to 2020. Compared with conventional fertilization, 75% OF significantly increased the nitrogen uptake and nitrogen utilization efficiency of winter wheat. Considering the economic and ecological factors, replacing 75% inorganic fertilizer with organic fertilizer can take into account the requirements of emission reduction, superior yield and high efficiency, and provide a reference for the better fertilization mode of winter wheat in Northwest China. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 46

Main heading: Emission control

Controlled terms: Carbon dioxide? - ?Elastin? - ?Greenhouse gas emissions? - ?Low emission

Uncontrolled terms: Combined application of organic and inorganic fertilizer? - ?Fertilizer use? - ?Global warming potential? - ?Greenhouses gas? - ?Inorganic fertilizers? - ?Nitrogen fertilizer use efficiency? - ?Organic fertilizers? - ?Use efficiency? - ?Winter wheat? - ?Winter wheat farmland

Classification code: 1502.1.1.4.1 Air Pollution Control? - ?1502.1.2 Climate Change? - ?203 Biomaterials? - ?804.2 Inorganic Compounds

Numerical data indexing: Percentage 1.00E+02%, Percentage 1.90E+01%, Percentage 2.50E+01%, Percentage 3.10E+01%, Percentage 3.80E+01%, Percentage 4.70E+01%, Percentage 5.00E+01%, Percentage 6.20E+01%, Percentage 6.40E+01%, Percentage 7.00E+00%, Percentage 7.30E+01%, Percentage 7.50E+01%, Percentage 7.80E+01%, Percentage 9.40E+01%, Size 1.016E-01m

DOI: 10.6041/j.issn.1000-1298.2025.02.044

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

23. Estimation Method and Experiment of Wheel Angle of Paddy Field Agricultural Machinery Based on Dual Observation Fusion Kaiman Filter

Accession number: 20251018008447

Title of translation: 基于双观测值融合卡尔曼滤波器的水田农机转向轮角估计方法与试验

Authors: Man, Zhongxian (1, 2); He, Jie (1, 2); Feng, Dawen (1, 2); Li, Renhao (1, 2); Deng, Xiaobing (1, 2); Tu, Tuanpeng (1, 2); Wang, Pei (1, 2); Hu, Lian (1, 2)

Author affiliation: (1) Key Lahoratory qf Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou; 510642, China; (2) State Key Lahoratory of Agricultural Equipment Technology, Guangzhou; 510642, China

Corresponding author: Hu, Lian(lianhu@scau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 38-47

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to solve the problem that the sudden change of the speed of automatio driving agricultural machinery in paddy field leads to inaccurate angle estimation, a steering wheel angle estimation method of agricultural machinery in paddy field was proposed based on dual Observation fusion Kaiman filter, and a steering wheel angle estimation model of agricultural machinery in paddy field was established. Firstly, the improved two-wheeled agricultural machinery sideslip model was used to obtain the front wheel steering angle of paddy agricultural machinery based on kinematics model. Secondly, the collected GPS speed and inertial navigation speed were compensated by weighted Observation fusion method. Finally, a method for estimating the front wheel steering angle of paddy agricultural machinery based on dual Observation fusion Kaiman filter was proposed, which took the front wheel steering angle based on kinematics model and the front wheel steering angle based on steering motor coding as dual Observation values, so as to estimate the front wheel steering angle of paddy agricultural maehinery. In order to verify the proposed method, speed eorrection, front wheel steering angle estimation test and linear traeking test were earried out in paddy field on the platform of rice direct seeding machine. The results of speed eorrection test showed that the unevenness of paddy field hard bottom layer was the direct reason for the poor fitting accuracy of front wheel angle. The proposed method stabilized the speed of direct seeding machine in a certain r?nge, and solved the problem of poor fitting accuracy of front wheel angle caused by the fluctuation of paddy field hard bottom layer. The front wheel steering angle estimation experiment showed that the average traeking error of the virtual wheel angle relative to the angle change of the angle sensor was 0. 12°, the maximum deviation was 1. 67° and the Standard deviation was 0. 4°. The method can accurately measure the steering angle of the front wheel of agricultural maehinery, and finally control the direct seeding machine to track the target angle stably, which met the accuracy requirements of estimation of front wheel angle of agricultural maehinery in paddy field. The results of linear traeking test showed that the average error was 3. 14 cm and the Standard deviation of position deviation was 2.11 cm in paddy field environment. The method proposed was suitable for unmanned paddy field, which improved the accuracy of corner estimation and the quality of agricultural maehinery navigation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Wheels

Controlled terms: Agricultural robots? - ?Cash registers? - ?Gas bearings? - ?Light velocity? - ?Screws? - ?Sprockets? - ?Turbines

Uncontrolled terms: Agricultural maehinery? - ?Angle estimation? - ?Autopilot? - ?Direct-seeding? - ?Estimation methods? - ?Front wheel angles? - ?Front wheel steering angles? - ?Kaiman filter? - ?Paddy fields? - ?Steering wheel

Classification code: 1007.1 Turbines and Steam Turbines? - ?1103.2 Computer Peripheral Equipment? - ?601.2 Machine Components? - ?602 Mechanical Drives and Transmissions? - ?605.2 Small Tools, Unpowered? - ?731.6 Robot Applications? - ?741.1 Light/Optics? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Size 1.40E-01m, Size 2.11E-02m

DOI: 10.6041/j.issn.1000-1298.2025.02.004

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

24. Behavioral Representation for Ammonia-nitrogen Stress of Epinephelus akaara for Embedded System

Accession number: 20251018009074

Title of translation: 赤点石斑鱼氨氮应激行为嵌入式表征研究

Authors: Nie, Pengcheng (1); Qian, Cheng (1); Wang, Qingping (1); Zeng, Guoquan (2); Ma, Jianzhong (2); Liu, Shijing (3)

Author affiliation: (1) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou; 310058, China; (2) Zhejiang Mariculture Research Institute, Wenzhou; 325000, China; (3) Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai; 200092, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 503-510 and 522

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The stress behavior representation based on research on ammonia nitrogen stress behavior is the premise and basis for realizing the recognition of ammonia nitrogen stress of Epinephelus akaara. However, most of the existing methods rely on high-performance hardware, which is not conducive to the embedded deployment and application of behavior representation methods in aquaculture. Taking symptoms such as reduced activity and imbalanced body of Epinephelus akaara under stress environment into account, a behavior representation method was proposed to represent the ammonia nitrogen stress behavior of Epinephelus akaara based on lightweight detection and tracking algorithm. In the detection algorithm, GhostV2 convolution was firstly used to lighten the feature extraction network of YOLO v5s. Then asymptotic feature pyramid network was integrated into the neck of YOLO v5s to support direct interactive fusion of different dimensional features. The results of ablation and comparison experiments showed that the accuracy and recall rate achieved 94. 3% and 89. 5% and mAP@ 0. 5 of the lightweight model was 96. 2%, which was 1. 6 percentage points higher than that of the original model while the model volume was about 60% of that of the original model. In the tracking algorithm, a lightweight ReID network was embeded into Ocsort and the appearance similarity matrix was introduced into the matching cost matrix in target association period. Comparison experiments showed that MOTA and IDF1 of improved tracking algorithm achieved 94. 7% and 69. 3%, which were 3. 2 percentage points and 6. 7 percentage points higher than that of the original Ocsort with YOLO v5s. Combined with the research on ammonia nitrogen stress behavior, average velocity and number of imbalanced Epinephelus akaara were selected to characterize the ammonia nitrogen stress behavior of Epinephelus akaara. The accuracy of identifying the behavior of Epinephelus akaara based on the characterization proposed method was 92. 2%, which can accurately classify whether the Epinephelus akaara was under ammonia nitrogen stress environment. The lightweight characterization method can be deployed on Jetson Orin Nano embedded system, with an average speed of 6 f / s, providing technical support for efficient and real-time identification of ammonia nitrogen stress in aquaculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Ammonia

Controlled terms: Agricultural robots? - ?Aquaculture? - ?Emotional intelligence? - ?Exponential functions? - ?Gluing? - ?Image annotation? - ?Matrix algebra? - ?Program debugging? - ?Shear bands

Uncontrolled terms: Ammonia-nitrogen? - ?Ammonia-nitrogen stress behavior representation? - ?Behavior representation? - ?Embedded deployment? - ?Epinephelu akaara? - ?Nitrogen stress? - ?Ocsort? - ?Percentage points? - ?Stress behavior? - ?YOLO v5

Classification code: 1106.1 Computer Programming? - ?1106.3.1 Image Processing? - ?1201.1 Algebra and Number Theory? - ?1201.2 Calculus and Analysis? - ?210 Adhesive Materials? - ?214 Materials Science? - ?214.1.1 Stress and Strain? - ?731.6 Robot Applications? - ?804.2 Inorganic Compounds? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?903.2 Information Dissemination

Numerical data indexing: Percentage 2.00E+00%, Percentage 3.00E+00%, Percentage 5.00E+00%, Percentage 6.00E+01%, Percentage 7.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.02.047

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

25. Design and Experiment of Flow-enhanced Mechanical Combining Harvester for Small-seeded Alfalfa

Accession number: 20251018008474

Title of translation: 小粒径苜蓿种子机械梳脱增流式收获机设计与试验

Authors: Pan, Kaoxin (1, 2); Zhang, Qing (1, 2); You, Yong (1, 2); Sun, Lihao (1, 2); Hu, Jianliang (3); Wang, Decheng (1, 2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Intelligent Crassland Equipment and Smart Crassland Research Center, China Agricultural University, Beijing; 100083, China; (3) Shijiazhuang Xinnong Machinery Co., Ltd., Shijiazhuang; 052400, China

Corresponding author: Zhang, Qing(zhangqingbit@cau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 342-356

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problem of lacking dedicated harvesting equipment and having low clean harvest rates for small-seeded alfalfa in mechanized harvesting both domestically and internationally, a half-feed alfalfa seed harvester combining meehanieal eombing Separation with negative pressure airflow suetion and flow enhaneement was designed. Based on the biological charaeteristics of alfalfa plants and planting agronomy, a mechanized harvesting scheme was proposed, including mechanical eombing Separation, airflow adsorption and transportation, and enhanced airflow through a fan for small-seeded alfalfa. According to the mechanism of loss suppression during the alfalfa seed harvesting process, key components of the eombing platform were designed. For uncombed losses during the alfalfa seed harvesting process, a eombing strueture was designed to perform eombing Separation of highly dispersed seed pods. For seed fall and splash losses, a drum-wall combination airflow throat and negative pressure fan System were designed to adsorb and transport the seeds and pods. On this basis, a kinematic analysis of the eombing detachment process was condueted. It was determined that the effective r?nge for the comb detaching speed ratio was between 36 and 50, with a comb detaching drum radius of 290 mm. The critical parameters affecting inoomplete combing losses were identified as the rotational speed of the comb detaching drum, the center-to-ground height, and the forward speed of the harvesting platform. Using CFD Simulation eombined with CCD orthogonal experimental methods, the interaction effects of the combing drum rotational speed, airflow throat dimensions, and negative pressure fan speed on the platform airflow field were explored. Using response surface methodology, the optimized values for the airflow throat dimensions, combing drum rotational speed, and negative pressure fan speed were determined to be 83 mm, 1 000 r/min and 1 450 r/min, respectively. To verify the effectiveness of the whole machine and the combing platform function, field trials of the alfalfa seed harvesting prototype were conducted. Results showed that when the height of the combing drum center above ground level was 430 mm and the machine’s travel speed was 0. 68 m/s, there was virtually no uncombed seed pod after combing, the alfalfa seed clean harvest rate reached 83.4%, and the operational efficiency was 0. 73 hm /h, meeting the mechanized harvesting index requirements for alfalfa seeds, providing a reference for the design of small-seeded seed harvesters. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Harvesters

Controlled terms: High pressure turbomachinery? - ?Light velocity? - ?Membrane technology? - ?Pressure gradient? - ?Screws? - ?Seed? - ?Solvent extraction

Uncontrolled terms: Alfalfa seeds? - ?Combing separation by mechanical tooth? - ?Harvest rates? - ?Mechanical? - ?Mechanized harvesting? - ?Negative pressure airflow? - ?Negative pressures? - ?Rotational speed? - ?Seed harvesting? - ?Small-seeded alfalpha

Classification code: 1007 Turbomachinery? - ?214 Materials Science? - ?605.2 Small Tools, Unpowered? - ?741.1 Light/Optics? - ?802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?941.7 Pressure Measurements

Numerical data indexing: Angular velocity 0.00E00rad/s, Angular velocity 7.515E+00rad/s, Percentage 8.34E+01%, Size 2.90E-01m, Size 4.30E-01m, Size 8.30E-02m, Velocity 6.80E+01m/s

DOI: 10.6041/j.issn.1000-1298.2025.02.032

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

26. Adaptive Three Variable Control Strategy of Electro-hydraulic Servo Damping Damper Loading Test Bench

Accession number: 20251018008439

Title of translation: 电液伺服减震阻尼器加载试验台自适应三状态控制策略研究

Authors: Qiang, Hongbin (1); Cheng, Zhangjian (1); Liu, Kailei (1); Kang, Shaopeng (1); Yang, Li (2)

Author affiliation: (1) School of Mechanical Engineering, Jiangsu University of Technology, Changzhou; 213001, China; (2) Jiangsu Rongda Shock Absorption Technology Co., Ltd., Changzhou; 213001, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 544-554

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problem that the traoking aocuraoy of the three variable Controller (TVC), whieh requires high precision of the Controller! model, is reduced due to the influence of System parameter changes and external interference on the loading test bench of electro-hydraulic damping damper. An adaptive three variable Controller (ATVC) was proposed, with a model reference adaptive control (MRAC) to compensate the loading System into an ideal stable reference model, and then an adaptive three variable control strategy was applied to the ideal reference model. According to the theoretical model of electro-hydraulic loading System of damping damper, the transfer function of the loading System considering the damping of the damping damper under test was derived. The model reference adaptive control System was designed based on Diophantine equation polynomial, and the loading System was compensated to realize the ideal reference model with non-minimum, which improved the System stability. The natural frequency and damping ratio of the loading System with a stable ideal reference model were compensated by three-state feedback, and pole assignment was realized by three-state feedforward to improve the dynamic characteristics of the System. Through the position step and sine response tests of the three-state control and the adaptive three-state control, under the action of the position Square wave loading signal of 2.0 mm/10 Hz, the overshoot was 13.5% and 9.6%, respectively, decreasing 28. 9%, and the rise time was 7. 4 ms and 5. 8 ms respectively, decreasing 21. 6%. The steady-state errors were 0.030 mm and 0.018 mm, respectively, which were decreased by 40.0%. Under the position sinusoidal loading signal of 1.5 mm/30 Hz, the displaoement errors were 0.29 mm and 0. 18 mm, respectively, which were decreased by 37. 9%. The adaptive three-state control can obviously improve the transient Performance index and displacement tracking accuracy of the electro-hydraulic servo loading test bench. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Model reference adaptive control

Controlled terms: Hydraulic servomechanisms? - ?State feedback? - ?Three term control systems

Uncontrolled terms: Adaptive Control? - ?Control strategies? - ?Electrohydraulic servos? - ?Loading system? - ?Loading tests? - ?Reference modeling? - ?State-control? - ?Test-bench? - ?Three variable controls? - ?Variable controllers

Classification code: 1401.2 Hydraulic Equipment and Machinery? - ?731.1 Control Systems? - ?731.7 Mechatronics

Numerical data indexing: Frequency 1.00E+01Hz, Time 4.00E-03s, Time 8.00E-03s, Size 1.50E-03m, Size 1.80E-02m, Size 1.80E-05m, Size 2.00E-03m, Size 2.90E-04m, Size 3.00E-05m, Frequency 3.00E+01Hz, Percentage 1.35E+01%, Percentage 4.00E+01%, Percentage 6.00E+00%, Percentage 9.00E+00%, Percentage 9.60E+00%

DOI: 10.6041/j.issn.1000-1298.2025.02.051

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

27. Yield Responses and Predictions of Future Change for Winter Wheat Summer Maize Based on Water-heat Coupling

Accession number: 20251018009061

Title of translation: 基于水热耦合的冬小麦夏玉米产量响应与变化预测

Authors: Ren, Hejing (1); Lu, Kaichao (2, 3); Cai, Jiabing (1); Hou, Lizhu (2)

Author affiliation: (1) China Institute of Water Resources and Hydropower Research, State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing; 100038, China; (2) School of Water Resources and Environment, China University of Geosciences, Beijing; 100083, China; (3) Ltd. of China Power Engineering Consulting Group, North China Power Engineering Co., Beijing; 100120, China

Corresponding author: Hou, Lizhu(hou_lz2002@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 429-443 and 484

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Global climate change will have a huge impact on future food production. Water and temperature are the most important environmental factors in the growth of winter wheat and summer maize, which significantly affect their yield. Based on the irrigation experimental data of winter wheat and summer maize in Baoding Irrigation Experimental Station in North China Plain from 2006 to 2015, the AquaCrop model was calibrated and validated, offering crop growth process simulations following local conditions. Being similar in structure of the four typical water production functions (Blank model, Stewart model, Jensen model, Minhas model), the water-heat production functions were set up between accumulated temperature, water consumption, and yield at each growth stage of winter wheat and summer maize. Using the data from the sixth version of the model for interdisciplinary research on climate (MIROC6) of the commentary on the coupled model intercomparison project (CMIP6), the daily rainfall and temperature data were downscaled to consider future climate change, including low carbon emission forcing scenario SSP1 RCP2 6 and SSP4 RCP3-4, medium carbon emission forcing scenario SSP2 RCP4-5, medium to high forcing emission scenario SSP3 RCP7-0 and high forcing scenario SSP5 RCP8-5. On this basis, the yields and their changes for winter wheat and summer maize in 2024-2064 were obtained and analyzed by the presented water-heat production function. Results showed that the AquaCrop model made good performances to simulate the growth process of winter wheat summer maize in this region after its calibration and verification by using ten years of irrigation test data. Among the four kinds of water-heat production functions constructed by the verified AquaCrop model simulation data, the Jensen type function had the highest output simulation accuracy. According to the water-heat production function, winter wheat was most sensitive to water during heading filling stage, and accumulated temperature during greening jointing stage had the most obvious effect on yield. Summer maize was most sensitive to water in jointing and heading period, and the accumulated temperature in this period had the most obvious effect on yield. Under the emission scenarios of SSP1 2-6, SSP2 4-5, SSP3 7-0, SSP4 3-4, and SSP5 8-5 in the five future climates, the potential yield of winter wheat tended to fluctuate, but it was higher than the current average potential yield. By the 2050s, the average potential yield of winter wheat would be 6-07 t/ hm2, 6-26 t/ hm2, 6-93 t/ hm2, 5-74 t/ hm2, and 5-95 t/ hm2, respectively. The overall potential yield of summer corn was on the rise, and by 2050s, the average annual potential yield of summer corn would reach 9-27 t/ hm2, 9-20 t/ hm2, 9-05 t/ hm2, 9-10 t/ hm2, and 9-24 t/ hm2, respectively. Overall, winter wheat and summer corn were more suitable for growth and development under SSP3 7-0 and SSP1 2-6 scenarios, respectively. Considering the hydrothermal conditions, the potential yield of winter wheat fluctuated down under the five climate scenarios, while the potential yield of summer maize showed an overall upward trend. Supplementary irrigation can bring about 70% of contribution rate to the potential yield of winter wheat. The contribution rate of rainfall during the growing period to the potential yield of summer maize was about 94%. The results can be used to evaluate the change of crop grain yield in this region under future climate change, and provide theoretical basis and technical support for the national strategy of ensuring food security. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 46

Main heading: Irrigation

Controlled terms: Film preparation? - ?Grain (agricultural product)? - ?Mining laws and regulations? - ?Quality assurance? - ?Quality control

Uncontrolled terms: Accumulated temperatures? - ?Aquacrop model? - ?CMIP6? - ?Heat coupling? - ?Heat production? - ?Summer maize? - ?Water- heat coupling? - ?Winter wheat? - ?Winter wheat summer maize? - ?Yield

Classification code: 208.4 Thin films? - ?502.1 Mine and Quarry Operations? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?902.3 Legal Aspects? - ?913.3 Quality Assurance and Control

Numerical data indexing: Percentage 7.00E+01%, Percentage 9.40E+01%, Size 2.032E-01m to 1.27E-01m

DOI: 10.6041/j.issn.1000-1298.2025.02.040

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

28. Design and Performance Test of Experimental Platform for Omnidirectional Control of Agricultural Chassis Center of Gravity in Hilly and Mountainous Areas

Accession number: 20251018008481

Title of translation: 丘陵山地农机底盘重心全向调控实验平台设计与性能试验

Authors: Sun, Jingbin (1); Meng, Xianzhe (1); Zeng, Lingkun (1); Zheng, Hang (2, 3); Ying, Jing (4, 5); Zhang, Haixin (1); Xu, Guangfei (1)

Author affiliation: (1) College of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng; 252000, China; (2) Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in Southeastern China Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Hangzhou; 310021, China; (3) Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou; 310021, China; (4) Key Laboratory of Agricultural Equipment Technology for Hilly and Mountainous Areas, Ministry of Agriculture and Rural Affairs, Chengdu; 610066, China; (5) Sichuan Academy of Agricultural Machinery Sciences, Chengdu; 610066, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 511-522

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The change of center of gravity position of mountain agricultural machinery seriously affects its stability, traction and obstacle crossing ability. Aiming at the problems of design theory of center of gravity control device of mountain agricultural machinery chassis and the lack of special experimental platform, an omnidirectional control experimental platform of center of gravity of mountain agricultural machinery chassis was designed. The theoretical analysis showed that the slope angle caused the shift of center of gravity of chassis of mountain farm machinery, which seriously affected the stability of contour line driving and longitudinal climbing Performance. Therefore, the overall structure of the experimental platform was determined by considering the relationship between slope angle and center of gravity position. The experimental platform mainly included inelination Simulation device and center of gravity adjustment device. Among them, the inelination Simulation device adopted the synergistic effect of multiple electric push bars to realize the omni directional slope Simulation of 0° ~ 15°, and the center of gravity adjustment device adopted the mode of “ H-shaped “ multi-slide combination to realize omnidirectional center of gravity adaptive adjustment, so as to realize the center of gravity adjustment funetion under different inelination conditions. The Performance test results showed that the mean value of simulated slope and the relative errors between data median line and simulated angle were within 0. 5° in horizontal, longitudinal and oblique slope Simulation of 0° ~ 15°. In the test, the maximum error of center of gravity position was -21. 4 mm. The center of gravity adaptive control can be achieved on the horizontal and vertical slopes of 0° ~ 15° and the oblique slopes of 0° ~ 12°. The average error of the test was 2. 6%, 3. 4% and 5. 9%, respectively, and the maximum error of the center of gravity adjustment was 6. 7 mm, 7. 3 mm and 10. 8 mm, respectively. The adaptive control of center of gravity can also be achieved on horizontal and longitudinal simulated slopes of 0° ~ 15° and oblique simulated slopes of 0° ~ 12°. The average test errors were 5. 4%, 6. 5% and 9. 7%, respectively, and the maximum errors of center of gravity adjustment were 9. 7 mm, 10. 3 mm and 15. 8 mm, respectively, which met the basic design requirements. The inelination Simulation and center of gravity adaptive control method proposed can provide reference for the research of center of gravity control theory of agricultural machinery chassis in hills and mountains. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 20

Main heading: Agricultural robots

Controlled terms: Traction control

Uncontrolled terms: Center of gravity? - ?Center of gravity adjustment? - ?Experimental platform? - ?Hilly and mountainous area agricultural chassi? - ?Hilly and mountainous areas? - ?Inelination simulation? - ?Maximum error? - ?Omnidirectional control? - ?Performance tests? - ?Test platforms

Classification code: 731.2 Control System Applications? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Percentage 4.00E+00%, Percentage 5.00E+00%, Percentage 6.00E+00%, Percentage 7.00E+00%, Percentage 9.00E+00%, Size 3.00E-03m, Size 4.00E-03m, Size 7.00E-03m, Size 8.00E-03m

DOI: 10.6041/j.issn.1000-1298.2025.02.048

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

29. Working Condition Optimization of Swirling Micro and Nano Bubble Generator for Aerated Drip Irrigation

Accession number: 20251018009091

Title of translation: 加气滴灌旋流式微纳米气泡发生器运行工况优化

Authors: Wang, Jian (1); Liu, Zhengliang (1); Wang, Duo (1); Chen, Rui (1); Wang, Xiuli (1)

Author affiliation: (1) Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, 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: 2

Issue date: February 2025

Publication year: 2025

Pages: 357-369 and 401

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aerated drip irrigation can effectively improve crop production efficiency and crop yield. The formation of a large number of uniform smaller micro-bubbles in the aerated system and the improvement of dissolved oxygen concentration in irrigation water are the key to the long distance uniform water and air transport of aerated drip irrigation. The working conditions of a micro- and nano-bubble generator designed was optimized based on the principle of cyclonic shear fragmentation. Through one-way test analysis, high-speed photography and dissolved oxygen meter, the influence of system operating pressure and air inlet on bubble characteristics (count, diameter and uniformity) and dissolved oxygen was studied. Finally, combined with the response surface RSM optimization test, the combination of operating pressure and air inlet under the optimal aerating effect of the device was determined. The results showed that the generation of bubbles and dissolved oxygen effect were better when the pressure was controlled within 300 ~ 400 kPa. In the range of 1% ~ 5% of air inlet, the best diameter distribution and the smallest average diameter were obtained with 1% of air inlet, while the rate of bubble generation was higher with 5% of air inlet. Meanwhile, the dissolved oxygen test showed that increasing the air intake significantly increased the dissolved oxygen efficiency as well as the final oxygen content. Through the response optimization solution, it was concluded that the plant could operate at a pressure of 350 kPa and an air intake of 2% with the lowest energy consumption. Under this condition, the final diameter of the small bubbles generated by the device was 68. 6 μm, the rate of oxygen enhancement was about 0. 81 mg / (L·min), the net increase in dissolved oxygen after stabilization was 1. 08 mg / L, and the bubble dissipation time was up to 323 s. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Air intakes

Controlled terms: Bubbles (in fluids)? - ?Oxygen regulators? - ?Subirrigation? - ?Water aeration

Uncontrolled terms: Bubble generators? - ?Condition? - ?Condition optimizations? - ?Drip irrigation? - ?Micro-nano bubble? - ?Micro/nano? - ?Microbubbles? - ?Nanobubbles? - ?Subsurface drip irrigation? - ?Working condition optimization

Classification code: 301.1.2 Gas Dynamics? - ?301.1.3 Aerodynamics (fluid flow)? - ?445.1 Water Treatment Techniques? - ?821.4 Agricultural Methods? - ?942.1.7 Special Purpose Instruments

Numerical data indexing: Mass 8.10E-05kg, Mass density 8.00E-03kg/m3, Percentage 1.00E00%, Percentage 2.00E+00%, Percentage 5.00E+00%, Pressure 3.00E+05Pa to 4.00E+05Pa, Pressure 3.50E+05Pa, Size 6.00E-06m, Time 3.23E+02s

DOI: 10.6041/j.issn.1000-1298.2025.02.033

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

30. Design and Experiment of Precision Spray-type Adaptive Weeder for Paddy Fields

Accession number: 20251018008470

Title of translation: 精准喷施型水田自适应除草机设计与试验

Authors: Wang, Jinfeng (1); Zhu, Pengyun (1); Chu, Yuhang (1); Xu, Chen (1); Song, Yuling (1); Wang, Yijia (2)

Author affiliation: (1) College of Engineering, Northeast Agricultural University, Harbin; 150030, China; (2) College of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin; 150030, China

Corresponding author: Wang, Yijia(yijiaw@connect.liku.hk)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 195-205

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Weed control in paddy fields is a key agronomio measure to improve rice yield, and chemical weed control is widely used beoause of its high efficiency. Traditional chemical weed control relies on manual Operation and often uses large area spraying, which increases the Operation cost and causes negative problems such as environmental pollution. Based on this background, a precision spraying type paddy weeder for adaptive weeding Operation was designed. The weeder spraying device and System were constructed, and the weed detection System with MS - YOLO v7 as the core framework was designed based on the constructed diversified paddy field weed dataset. The MS _ YOLO v7 model combined the backbone network with MobileOne, and replaced the CIoU loss function with the SIoU loss function. The model Performance was verified by ablation test and different model comparison test, and the results showed that the model recognition accuracy was 95.65%, the mean average precision (mAP) was 92. 67%, and the real-time Performance reached 51. 29 f/s. The IR model was reasoned by using OpenVINO on Raspberry Pi, and the detection of a single paddy field weed image took 0. 806 s. The constructed spraying System can instantly capture and analyze the transmission signals from the weed detection System, and then realize the precise regulation of the weed spraying device. The results of the field test showed that the precision spraying type paddy field adaptive weeder had a seedling injury rate of 2. 95%, a target application accuracy of 94. 98%, and a coefficient of Variation of 0. 128%, which met the agronomio requirements for weed control in paddy fields. The weeder realized the unmanned Operation of paddy field weeding and it ean provide teehnieal referenee for the intelligent development of agrieulture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Weed control

Controlled terms: Agricultural robots? - ?Bioremediation? - ?Fertilizers? - ?Herbicides

Uncontrolled terms: Adaptive? - ?Deep learning? - ?Detection system? - ?Loss functions? - ?Paddy fields? - ?Paddy weede? - ?Precision spraying? - ?Precision sprays? - ?Weed detection? - ?YOLO v7

Classification code: 1502.1 Environmental Impact and Protection? - ?1502.1.1.3 Soil Pollution? - ?1502.4 Biodiversity Conservation? - ?731.6 Robot Applications? - ?803 Chemical Agents and Basic Industrial Chemicals? - ?804.1 Organic Compounds? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals

Numerical data indexing: Percentage 1.28E+02%, Percentage 6.70E+01%, Percentage 9.50E+01%, Percentage 9.565E+01%, Percentage 9.80E+01%, Time 8.06E+02s

DOI: 10.6041/j.issn.1000-1298.2025.02.019

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

31. Online Prediction Method for Greenhouse Operation Chassis Driving Status by Digital Twins-driven

Accession number: 20251017996960

Title of translation: 基于数字孪生的温室作业底盘行驶状态在线监测方法

Authors: Wang, Minghui (1, 2); Xu, Jian (1); Zhou, Zhengdong (1); Wang, Yulong (1); Cui, Yongjie (1, 3)

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

Corresponding author: Cui, Yongjie(agriculturalrobot@nwafu.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 92-104

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Digital twin technology digitizes the entire lifecycle of physical entities to achieve monitoring and control of their states, providing a solution for remote control and continuous operation of robots. The high-precision control of the operation chassis during the driving process is the key to ensuring the quality of robot operation. Aiming to address the issues of large error in the driving state prediction model due to changes in greenhouse environment and chassis wear, as well as the difficulty of online dynamic data collection, a digital twin based online monitoring method for the driving state of the greenhouse operation chassis was proposed. Firstly, a digital twin system of the greenhouse operation chassis geared towards the driving state was developed. It dynamically perceived the dynamic data in the driving process online and simulated the change process of the chassis driving state in real time. Then an online prediction model of the driving state of the greenhouse operation chassis was constructed by combining the temporal deviation quantification model of the chassis driving state and considering various uncertain factors during the driving process. Finally, an experimental environment for online monitoring of the chassis driving state was set up, and online monitoring experiments and driving effect verification tests were carried out. The results showed that the online prediction method proposed corresponded to lateral offset prediction accuracies of 96. 32%, 95. 96%, 95. 69% and 96. 11% for datasets M1, M2, M3, M4, respectively. The longitudinal offset prediction accuracies were 96. 58%, 96. 36%, 96. 51% and 96. 13% for datasets M1, M2, M3, M4, respectively. Compared with the BP + SVR method, the prediction accuracy of lateral displacement was increased by 3. 61%, 3. 26%, 3. 92%, and 3. 98%, respectively, and the prediction accuracy of longitudinal displacement was increased by 2. 96%, 2. 78%, 3. 27%, and 3. 06%, respectively. This proved that the online prediction method proposed can effectively correct for the bias effects caused by ground fluctuations and chassis wear and tear. The average values of the actual lateral and longitudinal deviations of the chassis during driving were reduced by 48. 13% and 49. 49%, respectively compared with the chassis driving method based on fixed driving parameters. This method can dynamically adjust based on the real-time driving status of the chassis. The online monitoring method for greenhouse operation chassis driving status based on digital twins had the characteristics of strong real-time and high accuracy, which can provide a basis and reference for the continuous operation method and technology of robots in facility agriculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Agricultural robots

Controlled terms: Fertilizers? - ?Prediction models

Uncontrolled terms: Continuous operation? - ?Driving state? - ?Driving status? - ?Facility agriculture robot? - ?Facility agricultures? - ?Online prediction? - ?Prediction accuracy? - ?Prediction methods? - ?Time-varying models? - ?Wheeled chassi

Classification code: 1101 Artificial Intelligence? - ?1502.1.1.3 Soil Pollution? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals

Numerical data indexing: Percentage 1.10E+01%, Percentage 1.30E+01%, Percentage 2.60E+01%, Percentage 2.70E+01%, Percentage 3.20E+01%, Percentage 3.60E+01%, Percentage 4.90E+01%, Percentage 5.10E+01%, Percentage 5.80E+01%, Percentage 6.00E+00%, Percentage 6.10E+01%, Percentage 6.90E+01%, Percentage 7.80E+01%, Percentage 9.20E+01%, Percentage 9.60E+01%, Percentage 9.80E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.009

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

32. Design and Test of Non-destructive Rapid Testing Device for Internal Quality of Yam

Accession number: 20251018008465

Title of translation: 山药内部品质无损快速检测装置设计与实验

Authors: Wang, Wei (1, 2); Li, Yongyu (1, 2); Peng, Yankun (1, 2); Ma, Shaojin (1, 2); Wu, Jifeng (1, 2); Zhang, Yuexiang (1, 2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) National Agricultural Products Processing Technology and Equipment R&D Sub-center, Beijing; 100083, China

Corresponding author: Li, Yongyu(yyli@cau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 495-502

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: As a tuber orop with the same origin as medioine and food, yam is becoming more and more favored by people. With the development of yam proeessing industry, rapid non-destruetive testing and grading of yam quality is of great practica! significance to the healthy development of the industrial chain. For the purpose of developing a multi-quality non-destructive rapid detection device for yam, based on the principle of visible/near-infrared local diffuse transmission, a special detection probe for yam was designed according to the special appearance characteristics of yam, and a hand-held multi-quality nondestructive testing device for yam was developed by designing the optical path through comparative experiments. The Overall dimensions of the device were 150 mm X 80 mm X 150 mm and the weight was about 590 g. Based on the spectral information of 150 yams collected by the R&D device, the collected spectra were corrected by multiplicative scatter correction (MSC), and then the characteristic wavelengths were screened by the shuffled frog leaping algorithm (SFLA) to establish partial least Squares regression (PLSR) prediction model of dry matter, starch and protein content of yams, the correlation coefficients of the Validation set of dry matter, starch and protein were 0. 965 3, 0. 967 5 and 0. 956 3, respectively, and the root mean Square errors were 1. 09%, 0. 83% and 0. 15%, respectively. Based on the Qt development tool, the real-time analysis and control Software was written in C language, and the prediction model was implanted into the device for external verifioation. The dry matter, starch and protein Contents of 50 yam samples not partieipating in the modeling were detected five times by using the R&D device, and the coefficients of Variation were 1. 0% ~ 1. 2%, 1. 5% ~ 1. 7% and 1. 4% ~ 1. 6%, respectively. The absolute values of the maximum residuals of the dry matter, starch and protein of 50 yam samples were 1. 83%, 1. 64% and 0. 26%, respectively. The results showed that the handheld yam multi-quality non-destructive testing device can meet the needs of real-time detection in the field. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: C (programming language)

Controlled terms: Case hardening? - ?Failure analysis? - ?Fiber optic sensors? - ?Image texture? - ?Infrared transmission? - ?Network performance? - ?Problem oriented languages? - ?Visual languages

Uncontrolled terms: Diffuse transmission? - ?Dry matter content? - ?Local diffuse transmission? - ?Non destructive? - ?Non destructive testing? - ?Prediction modelling? - ?Quality parameters? - ?Starch contents? - ?Testing device? - ?Yam

Classification code: 1105 Computer Networks? - ?1106.1.1 Computer Programming Languages? - ?1106.3.1 Image Processing? - ?201.7.1 Heat Treatment Processes? - ?214.1 Mechanical Properties of Materials? - ?741 Light, Optics and Optical Devices? - ?741.1.2 Fiber Optics

Numerical data indexing: Mass 5.90E-01kg, Percentage 0.00E00%, Percentage 1.50E+01%, Percentage 2.00E+00%, Percentage 2.60E+01%, Percentage 4.00E+00%, Percentage 5.00E+00%, Percentage 6.00E+00%, Percentage 6.40E+01%, Percentage 7.00E+00%, Percentage 8.30E+01%, Percentage 9.00E+00%, Size 1.50E-01m, Size 8.00E-02m

DOI: 10.6041/j.issn.1000-1298.2025.02.046

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

33. Local Path Planning for Agricultural Robots Based on DAV_DWA

Accession number: 20251018008489

Title of translation: 基于DAV_DWA算法的农业机器人局部路径规划

Authors: Wang, Xiaochan (1, 2); Qi, Zihan (1); Yang, Zhenyu (1); Wang, Dezhi (1); Huang, Huixing (1); Lu, Meiguang (1)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 210031, China; (2) Jiangsu Province Engineering Lab for Modern Facility Agriculture Technology and Equipment, 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: 2

Issue date: February 2025

Publication year: 2025

Pages: 105-114

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to solve the ourrent stage of agricultural robots in the working ohannel of the demonstration greenhouse, dynamic obstacle processing is diffioult, poor target accessibility, easy to fall into the looal minimum and so on, the dual obstacle cost function, adaptive weights and virtual target_ dynamic window approach (DAV_DWA) was proposed to achieve greenhouse robot local path planning. Firstly, a dynamic - static dual-strategy obstacle avoidance method was adopted, which divided the safety distance of dynamic and static obstacles into two evaluation functions to reduce the collision risk of dynamic obstacles and prevent excessive obstacle avoidance of static obstacles. Secondly,an adaptive strategy for evaluation function weights was proposed, adaptive adjustment of the weights of each evaluation function according to two obstacle distances to enhance the robot’s path-finding ability in different complex environments. Finally, the virtual goal method was proposed to enable it to continue navigation after detaching from the local minimum,so as to enhance its path planning ability for the local minimum. Comparative Simulation experiments and greenhouse experiments were carried out, and the results showed that compared with other algorithms, DAV_DWA was able to reach the target point with a shorter path in a shorter time under the premise of guaranteeing the safety; in the greenhouse scenario, the robot can complete the autonomous navigation task,and the positioning error was no more than 0. 12 m, and tracking error was no more than 0. 10 m,which was in line with the practical requirements. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Cost functions

Controlled terms: Agricultural robots? - ?Fertilizers? - ?Motion planning? - ?Risk perception? - ?Robot programming

Uncontrolled terms: Agricultural robot? - ?Dynamic obstacles? - ?Dynamic window method? - ?Dynamic windows? - ?Evaluation function? - ?Local minimums? - ?Local path-planning? - ?Obstacles avoidance? - ?Parameter adaptation? - ?Window methods

Classification code: 1101 Artificial Intelligence? - ?1106.1 Computer Programming? - ?1201.7 Optimization Techniques? - ?1502.1.1.3 Soil Pollution? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals? - ?914.1 Accidents and Accident Prevention

Numerical data indexing: Size 1.00E+01m, Size 1.20E+01m

DOI: 10.6041/j.issn.1000-1298.2025.02.010

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

34. Simulation and Optimization Test of Imitation Furrowing Process of Cotton Direct Seeding Machine Based on DEM MBD Coupling

Accession number: 20251018008498

Title of translation: 基于DEM-MBD耦合的棉花直播机仿形开沟过程

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

Corresponding author: Ding, Youchun(kingbugl63@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 275-289 and 341

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the existing direct seeding equipment in the cotton area of the Yangtze River Basin, which has poor imitation effect, low seeding depth qualification index, and unsatisfactory consistency of seeding depth, an independent imitation cotton direct seeding machine was designed. The discrete element method and multi-body dynamics coupling method were used to simulate and analyze the Operation process of imitation furrowing, and the key structure and working parameters affecting the depth of seeding furrow were determined. A one-factor quadratic orthogonal rotational combination test was conducted with the qualified index of furrowing depth and the coefficient of Variation of furrowing depth consistency as evaluation indexes, and the optimal parameter combinations were determined to be the spring stiffness of 15 N/mm and the operating speed of 0. 8 m/s. Under the conditions of optimal parameter combinations, the qualified index of furrowing depth was 86.7% and the coefficient of Variation of furrowing depth consistency was 11.6%. The results of field test under the optimal parameter combination showed that the qualified index of furrowing depth was 84%, the coefficient of Variation of furrowing depth consistency was 12. 80%, and the relative errors of field and Simulation of qualified index of furrowing depth and coefficient of Variation of furrowing depth consistency were 3. 21% and 9. 38%.Optimized parameter combinations and other combinations of field test results were as follows; open furrow depth qualified index was increased by not less than 6 percentage points, open furrow depth consistency coefficient of variation was decreased by not less than 1.69 percentage points, and the field emergence effect can meet the requirements of the Yangtze River Basin cotton area live operation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Main heading: Couplings

Uncontrolled terms: Coefficients of variations? - ?DEM — MBD coupling? - ?Direct-seeding? - ?Field test? - ?Optimal parameter combinations? - ?Percentage points? - ?Profi 11 ing mechanism? - ?Seeder? - ?Seeding depth? - ?Yangtze River basin

Classification code: 601.2 Machine Components? - ?602.1 Mechanical Drives

Numerical data indexing: Percentage 1.16E+01%, Percentage 2.10E+01%, Percentage 3.80E+01%, Percentage 8.00E+01%, Percentage 8.40E+01%, Percentage 8.67E+01%, Surface tension 1.50E+04N/m, Velocity 8.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2025.02.026

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

35. Multi-machine Collaborative Control Method of Agricultural Machinery Formation Transfer Based on Fuzzy Algorithm

Accession number: 20251018008463

Title of translation: 基于模糊算法的农机编队转场多机协同控制方法

Authors: Wei, Xinhua (1, 2); Deng, Yi (3); Cui, Xinyu (3); Wang, Yefei (3); Zhang, Shaocen (3); Yang, Jiaxin (3)

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

Issue date: February 2025

Publication year: 2025

Pages: 48-60

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of slow response speed, low control accuracy, poor stability, and insufficient robustness caused by the influenee of eomplex environment of collaborative control in agricultural machinery transfer scenarios, a multi-machine collaborative control method is proposed for agricultural machinery formation transfer. The multi-machine collaborative model was constructed in which the host machine drived and navigated manually and the slave machines followed automatically. Based on the Frenet coordinate transformation, the collaborative control was decoupled into horizontal and vertical control, and the longitudinal Controller was designed by using the model predictive control algorithm to achieve the maintenance of the relative distance and the following of speed and acceleration among the units, and the horizontal Controller was designed by using the pure tracking algorithm to achieve the slave machines to follow the trajectory of the host machine and the introduction of fuzzy algorithm adjusted the key control coefficients to optimize the control effect in real time. Based on the CarSim/Simulink platform to design a variety of transfer typical working conditions on the designed method for Simulation test analysis, comparisons showed that compared with the traditional control methods it had a more reliable and superior Performance, and based on the intelligent tractor unit to carry out the real vehicle test verification, the results showed that the relative speed error was less than 0.570 m/s, the relative distance error was less than 0. 169 m, the acceleration error was less than 0. 252 m/s, the lateral error was less than 0. 090 m, all of them can gradually and steadily meet the aetual needs of agrieultural maehinery formation transfer. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Tractors (agricultural)

Controlled terms: Agricultural robots? - ?Fertilizers? - ?Predictive control systems

Uncontrolled terms: Agrieultural maehinery transfer? - ?Collaborative control? - ?Control methods? - ?Fuzzy algorithms? - ?Model predietive eontrol algorithm? - ?Multi-machines? - ?Multi-maehine eollaborative? - ?Pure-pursuit algorithms? - ?Relative distances? - ?Slave machines

Classification code: 1502.1.1.3 Soil Pollution? - ?731.1 Control Systems? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals

Numerical data indexing: Size 1.69E+02m, Size 9.00E+01m, Velocity 2.52E+02m/s, Velocity 5.70E-01m/s

DOI: 10.6041/j.issn.1000-1298.2025.02.005

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

36. Velocity Control for Autonomous Following Platform Walking Speed Based on DBO Optimized BP PID Algorithm

Accession number: 20251018008456

Title of translation: 基于蜣螂优化BP-PID的温室自主跟随平台行走速度控制研究

Authors: Xiao, Maohua (1); Chen, Tai (1); Zhuang, Xiaohua (2); Zhu, Yejun (1); Hu, Yibin (1); Wang, Hongxiang (3)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 210031, China; (2) SangpuAgricultural Machinery (Changzhou) Co., Ltd., Changzhou; 213200, China; (3) Jiangsu Electronic Information Vocational College, Huaian; 223003, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 83-91 and 154

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: This study addresses the issues of complexity in greenhouse operational environments and poor stability of existing mechanical Walking Systems by conducting researoh on the autonomous following electrie platform Walking speed control in greenhouses. Due to the system’s inherent nonlinearity and time-varying eharaeteristics, traditional PID eontrol algorithms fail to aehieve effective control. Therefore, a düng beetle optimizer (DBO) optimized BP neural network PID control algorithm was proposed. This algorithm optimized the weights of the BP neural network by using the DBO algorithm, thereby accelerating the self-learning rate of the BP neural network. It achieved rapid and precise control of the greenhouse autonomous following electrie platform Walking speed, enhanced System response speed, and reduced overshoot. Experimental results demonstrated that at a Walking speed of 1 m/s, the System exhibited an average response speed of 0. 11 s, settling time of 0. 27 s, and a maximum overshoot of 2. 44%. When there were changes in track speed and direction, the System maintained advantages of fast response, minimal overshoot, and oscillation-free steady-state process. Compared with PID control algorithm, BP - PID control algorithm, GA - PID control algorithm, ACO _ PID control algorithm, the DBO - BP - PID control algorithm showed superior Performance in control stability and precision, effectively mitigating System hysteresis and nonlinear effeets, thereby meeting the control requirements for greenhouse autonomous following electric platform Walking speed. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Proportional control systems

Controlled terms: Speed regulators? - ?Time varying control systems? - ?Two term control systems

Uncontrolled terms: Autonomous following electrie platform? - ?BP - PID eontrol? - ?BP neural networks? - ?Dung beetle optimization algorithm? - ?Dung beetles? - ?Optimization algorithms? - ?Optimizers? - ?PID control algorithm? - ?Walking speed? - ?Walking speed control

Classification code: 601.2 Machine Components? - ?704.2 Electric Equipment? - ?731.1 Control Systems? - ?732.1 Control Equipment

Numerical data indexing: Percentage 4.40E+01%, Time 1.10E+01s, Time 2.70E+01s, Velocity 1.00E00m/s

DOI: 10.6041/j.issn.1000-1298.2025.02.008

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

37. Design and Experiment of VS-1D CNN-based Clearing Loss Detection System for Corn Kernel Direct Harvester

Accession number: 20251018009050

Title of translation: 基于VS 1D CNN 的玉米籽粒直收机清选损失检测系统设计与试验

Authors: Xing, Gaoyong (1, 2); Ge, Shicong (2); Lu, Caiyun (1); Zhao, Bo (1, 2); Liu, Yangchun (2); Zhou, Liming (2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) State Key Laboratory of Agricultural Equipment Technology, Beijing; 100083, China

Corresponding author: Lu, Caiyun(lucaiyun@cau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 206-216

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of arduous threshold delineation, inadequate robustness, and insufficient adaptability of conventional clearing loss detection sensors that depend on temporal domain feature thresholds to distinguish kernel impact signals, a comprehensive clearing loss detection system for corn kernel direct collectors was developed, and a kernel impact classification algorithm predicated on a variable scale one-dimensional convolutional neural network (VS 1D CNN) was proposed. Initially, the hardware circuitry and software processing program were engineered for impact signal acquisition, processing, and transmission, alongside the development of the supporting host computer. Subsequently, a data acquisition testing platform was established to gather and archive the impact signals of weeds and maize kernels under varying impact heights and angles, thereby constructing a data set and training the VS 1D CNN seed impact classification algorithm, with the training outcomes indicating that the model’s accuracy was 94.2% on the testing set. Ultimately, the efficacy of the devised detection system under diverse operational conditions and the classification performance of distinct stray residues and seed mixtures were validated, with results demonstrating that the proposed VS 1D CNN algorithm performed commendably, achieving detection accuracy exceeding 95% across different installation sites and varying seed flow rates; the classification accuracy for identifying different proportions of stray residues and seed mixtures surpassed 93%, signifying that the proposed algorithm exhibited exceptional performance. This underscored that the algorithm delineated in this manuscript possessed remarkable efficacy and can accurately detect seed losses without establishing a fixed temporal domain feature threshold. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Convolutional neural networks

Controlled terms: Computer testing? - ?Digital storage? - ?Image segmentation? - ?Network security? - ?Spatio-temporal data

Uncontrolled terms: 1d CNN? - ?Clearing loss? - ?Convolutional neural network? - ?Corn kernel direct harvester? - ?Corn kernels? - ?Deep learning? - ?Detection system? - ?Impact signals? - ?Loss detection? - ?One-dimensional

Classification code: 1101.2.1 Deep Learning? - ?1103 Computer Systems and Equipment? - ?1103.1 Data Storage, Equipment and Techniques? - ?1106 Computer Software, Data Handling and Applications? - ?1106.3.1 Image Processing? - ?1106.4 Database Systems

Numerical data indexing: Percentage 9.30E+01%, Percentage 9.42E+01%, Percentage 9.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.020

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

38. Hierarchical Cooperative Control Strategy of Unmanned HMCVT Tractor Considering Trajectory Tracking Performance and Economy

Accession number: 20251017996947

Title of translation: 考虑轨迹跟踪性能与经济性的无人驾驶 HMCVT 拖拉机分层协同控制策略研究

Authors: Xu, Liyou (1, 2); Tao, Yuan (1); Zhang, Junjiang (1, 2); Wen, Changkai (3); Wang, Dongqing (2); Liu, Mengnan (2); Yan, Xianghai (1)

Author affiliation: (1) College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang; 471003, China; (2) YTO Group Co., Ltd., Luoyang; 471039, China; (3) College of Engineering, China Agricultural University, Beijing; 100083, China

Corresponding author: Zhang, Junjiang(zhangjunjiang2020@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 61-72 and 123

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Most of the unmanned tractors in the process of trajectory tracking focus on the tracking performance, ignoring the operational energy consumption resulting in poor economy. Aiming at the above problems, a layered collaborative control strategy that considered economy and trajectory tracking performance was proposed. Firstly, the trajectory tracking system was established by model predictive control algorithm with longitudinal deviation and transverse deviation as the target and acceleration and front wheel angular speed as the constraints, and secondly, the binary regulating economic control strategy based on external parameter optimization was established by taking the ratio of engine fuel consumption rate and hydro-mechanical CVT transmission efficiency as the optimization target. On this basis, the trajectory tracking system and economic control strategy were integrated to form a hierarchical cooperative control strategy with the predicted speed of the tractor at the next moment and the current plow resistance of the tractor as the transfer variables. Pure tracking and one-dimensional economic control strategy were used as the comparison strategy, and the cooperative control strategy was simulated based on the Matlab simulation platform, and the effectiveness of the cooperative control strategy was verified by the hardware-in-the-loop test platform. The results showed that compared with the comparison strategy, the layered cooperative control strategy effectively reduced the trajectory tracking deviation of the unmanned tractor and improved the tractor’s economy, the speed variance was reduced by 36. 7%, the longitudinal tracking deviation was reduced by 89. 8%, the lateral tracking deviation was reduced by 91. 7%, and the tractor fuel consumption was reduced by 11. 8% . ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: MATLAB

Controlled terms: Agricultural robots? - ?Predictive control systems? - ?Tractors (agricultural)? - ?Tractors (truck)? - ?Variable speed transmissions

Uncontrolled terms: Co-operative control? - ?Continuously variable transmission? - ?Control strategies? - ?Cooperative control strategy? - ?Hydro-mechanical? - ?Hydro-mechanical continuously variable transmission? - ?Model predictive control algorithm? - ?Model-predictive control? - ?Predictive control algorithm? - ?Trajectory-tracking ? - ?Unmanned tractor

Classification code: 1106.5 Computer Applications? - ?1201.5 Computational Mathematics? - ?602.2 Mechanical Transmissions? - ?663.1 Heavy Duty Motor Vehicles? - ?731.1 Control Systems? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Percentage 7.00E+00%, Percentage 8.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.02.006

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

39. Remote Sensing Crop Classification Based on Objective Weighting Method and Ensemble Learning

Accession number: 20251018008486

Title of translation: 基于客观赋权法和集成学习的作物遥感分类研究

Authors: Xun, Lan (1); Xie, Yi (1)

Author affiliation: (1) College of Geographical Sciences, Shanxi Normal University, Taiyuan; 030031, China

Corresponding author: Xie, Yi(xieyi@sxnu.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 370-380

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aoourate crop map is critical for agricultural monitoring and related decision-making. Although many algorithms were adopted for erop Classification, the Performance of individual classifier varied with study area and data used. Aiming to address this issue, a novel ensemble learning (EL) framework was developed, which adopted objective weighting methods to assign weights to five widely used classifiers, including K-nearest neighbor, support vector machine, random forest, back propagation neural network and convolutional neural network. Four study sites in the United States were selected to examine the Performance of the proposed EL framework. Time series of normalized difference Vegetation index derived from Sentinel - 2 multispectral instrument images were used as the input features for crop Classification. Two modified objective weighting methods, termed modified entropy method (mEN) and modified coefficient of Variation method (mCV), were proposed to determine the weights of base classifiers. The probability Outputs of base classifiers were combined with weights to determine the final label. The results showed that weights assigned by modified weighting methods were more reasonable than those by original weighting methods in multi-classifier ensemble. The combination of mEN and mCV (mEN - mCV) further amplified the weight difference of base classifiers, and achieved an improved Performance than single weighting method. Compared with five base classifiers, Overall accuracy achieved by mEN - mCV with Fl-score as input (mEN - mCV - F) was increased by 1. 12 ~ 6. 45 percentage points, 0.75 ~ 3. 98 percentage points, 0.45 ~ 2. 70 percentage points and 1.15 ~ 2. 50 percentage points at four sites, respectively. The advantage of the proposed EL framework over unweighted (i.e., majority vote, probability fusion) and accuracy-weighted methods was that both Classification accuracy and stability of base classifiers were considered, thus resulting in a higher Performance. These results indicated that the proposed EL framework had potential in improving the accuracy of crop Classification. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Convolutional neural networks

Controlled terms: Agricultural robots? - ?Nearest neighbor search? - ?Support vector machines

Uncontrolled terms: Base classifiers? - ?Crop classification? - ?Ensemble learning? - ?Entropy methods? - ?Learning frameworks? - ?Objective weighting method? - ?Percentage points? - ?Performance? - ?Remote-sensing? - ?Weighting methods

Classification code: 1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1201.7 Optimization Techniques? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment

DOI: 10.6041/j.issn.1000-1298.2025.02.034

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

40. Image Recognition Method for Lepidoptera Pests Based on Few-shot Learning

Accession number: 20251018008442

Title of translation: 基于小样本学习的鳞翅目害虫图像识别方法

Authors: Yang, Xinting (1, 2); Zhou, Zijie (1, 2); Li, Wenyong (2, 3); Chen, Xiao (2, 4); Wang, Hui (2, 5); Yu, Helong (1)

Author affiliation: (1) College of Information Technology, Jilin Agricultural University, Changchun; 130118, China; (2) Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (3) National Engineering Research Center for Information Technology in Agriculture, Beijing; 100097, China; (4) College of Information Technology, Shanghai Ocean University, Shanghai; 201306, China; (5) School of Information Science and Engineering, Shandong Agricultural University, Taian; 271018, China

Corresponding author: Yu, Helong(yulielong@jlau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 402-410

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In real-world soenarios where pest data is scarce, existing pest image recognition methods are prone to overfitting, resulting in insuffioient model expressiveness. To address this issue, a novel few-shot field pest image Classification method that integrated metric learning with transfer learning was proposed. Firstly, the ECA - Pyramid - ResNetl2 model was pretrained on the mini - ImageNet dataset. Subsequently, PN was chosen as the classifier, and cosine similarity was selected as the distance metric. The ECA channel attention mechanism was then incorporated into the metric module to enhance pest image feature representation by capturing inter-channel dependencies, with a kernel size of 3. Additionally, a feature pyramid structure was employed to capture the local and multi-scale features of pest images. After evaluating different pooling combinations, the 2x2+4x4 pooling combination was selected. Finally, a meta-dataset comprising 20 self-built categories of Lepidoptera pest images was utilized for meta-training and meta-testing of the model. Experimental results demonstrated that under 3 -way 5 - shot and 5 - way 5 - shot conditions, the proposed method achieved accuracy rates of 91. 16% and 87.26%, respectively, surpassing the most relevant works of the past two years, SSFormers and DeepBDC, by 4.58 percentage points and 1.35 percentage points. The proposed model effectively enhanced the feature representation of target images in few-shot learning, providing a methodological reference for the automatic identification of field pests in data-scarce scenarios. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Zero-shot learning

Controlled terms: Adversarial machine learning? - ?Contrastive Learning? - ?Image classification? - ?Transfer learning

Uncontrolled terms: ECA? - ?Few-shot learning? - ?Lepidoptera? - ?Metric learning? - ?Percentage points? - ?Pest recognition? - ?Pests images? - ?Pyramidfcn? - ?Recognition methods? - ?Transfer learning

Classification code: 1101.2 Machine Learning? - ?1106.3.1 Image Processing

Numerical data indexing: Percentage 1.60E+01%, Percentage 8.726E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.037

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

41. Design and Experiment of Positive Pressure Airflow Guide Groove Seed Guiding Device for Maize Detal-row High-speed Precision Seeder

Accession number: 20251018009014

Title of translation: 玉米品字形高速精量播种机正压气流导槽式导种装置设计与试验

Authors: Yi, Shujuan (1); Zhang, Yupeng (1); Dai, Zhibo (1); Kong, Lingtong (1); Sun, Wensheng (1); Xu, Lei (2)

Author affiliation: (1) College of Engineering, Heilongjiang Bayi Agricultural University, Daqing; 163319, China; (2) Heilongjiang Beidahuang Modern Agricultural Service Group Co., Ltd., 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: 2

Issue date: February 2025

Publication year: 2025

Pages: 261-274

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In view of the current 2BDQ maize delta-row seeder high-speed operation (12 ~ 16 km/h) due to the airflow wrapped seeds into the bilateral seed guide device speed is too fast to produce a violent collision, so that the bilateral seed staggered uniformity was decreased, resulting in the problem of low forming rate of the falling seed, a positive pressure air guide groove type seed guide device was designed. The structure of the seed guiding device and the principle of the upper and lower combined seed guiding groove were expounded. The mechanical model of the introduction and attachment process of corn seeds on the guide groove was established, and the relationship between the seed constraint state and the structural parameters of the guide groove during the seed guiding process was explored. The DEM ? CFD coupling simulation method was used to analyze the speed trajectory curve of corn seed introduction to determine the width ratio of the upper guide groove and the lower guide groove, and the range of the upper guide groove slotting height, the lower guide groove width and the positive pressure value of the conveying airflow was determined by single factor test. Taking the range of single factor test results as the level value of test factors, the qualified index of glyph and the coefficient of variation of seed introduction consistency as the test indexes, the Box ? Behnken design bench test of three factors and three levels was carried out by using JPS ? 16 high-speed seeding performance test bench, and the optimal parameter combination of seed introduction device was obtained and verified by test. Four kinds of seeds were selected to carry out the suitable sowing test of the device. The results showed that the optimal parameter combination was the upper guide groove slotting height of 354. 491 mm, the lower guide groove width of 2. 245 mm, and the positive pressure value of the conveying air flow of 0. 739 kPa. Under the verification test, the qualified index of the parameter combination was 91. 791%, and the variation coefficient of the seed guiding consistency was 6. 111%, which was consistent with the optimization results. In the field test, the qualified index of the glyph was not less than 85. 1% at working speed of 12 ~ 16 km/h. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Slotting

Controlled terms: Guides (mechanical)? - ?Guideways? - ?Metal castings

Uncontrolled terms: Corn seeds? - ?Delta-row? - ?Guide device? - ?Guide groove? - ?Guiding device? - ?High Speed? - ?High-speed precision seeder? - ?Maize? - ?Positive pressure? - ?Seed guide device

Classification code: 201.4.2 Foundry Practice? - ?601 Mechanical Design? - ?604.1 Metal Cutting

Numerical data indexing: Percentage 1.00E00%, Percentage 1.11E+02%, Percentage 7.91E+02%, Pressure 7.39E+05Pa, Size 1.20E+04m to 1.60E+04m, Size 2.45E-01m, Size 4.91E-01m

DOI: 10.6041/j.issn.1000-1298.2025.02.025

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

42. Greenhouse Scene Recognition Method Based on Local Image Feature Aggregation

Accession number: 20251018009068

Title of translation: 基于局部图像特征聚合的温室场景识别方法

Authors: Yu, Meiling (1); Zhou, Yuncheng (1); Hou, Yuhan (1); Liu, Junting (1)

Author affiliation: (1) College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang; 110866, China

Corresponding author: Zhou, Yuncheng(zhouyc2002@syau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 485-494

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Scene recognition could be used as an alternative for spatial positioning in greenhouse environments, and it was also one of the important functions of the visual system of intelligent agricultural machinery equipment. Addressing the issue that scene recognition paradigms based on feature clustering could not adapt to the recognition of greenhouse scenes with high dynamic changes and high similarity, a greenhouse scene recognition method based on deep feature aggregation was proposed. This method, grounded on a pre-trained visual transformer network, extracted local features from scene images. It applied the global receptive field characteristics of multi-layer perceptron, took into account the spatial relationships of local features, fused the local features of the images, and generated global descriptors for the scene images. With the goal of minimizing multi-similarity loss as the optimization objective, a greenhouse scene recognition model was constructed. The test results indicated that the R@ 1 (top - 1 recall rate), R @ 5, and R @ 10 of the model’s scene recognition reached 78. 43%, 89. 21%, and 92. 47%, respectively, and it possessed high scene recognition accuracy. The proposed feature mixing method based on multi-layer perceptron was proven effective, with an improvement of 8. 01 percentages in R@ 1 compared with that of feature aggregation using pooling operations. The model demonstrated a certain robustness to changes in lighting conditions, with the R@ 1 metric decreased by no more than 4. 00 percentages under strong and weak lighting conditions compared with that under normal medium lighting conditions. Changes in camera angle and sampling distance also impacted the model’s recognition performance, with a decline of 6. 61 percentages for angle changes within 20 degrees, and a drop of 17. 87 percentages for distance changes within twice the original distance. Compared with the existing scene recognition benchmark methods, including NetVLAD, GeM, Patch - NetVLAD, MultiRes - NetVLAD, and MixVPR, the R@ 1 of proposed model was improved by 7. 82, 6. 59, 3. 56, 4. 14, and 1. 88 percentages, respectively, demonstrating a significant performance enhancement on the greenhouse scene recognition task. The image global feature aggregation method based on multi-layer perceptron constructed was able to generate reliable global descriptors for greenhouse scene recognition, and exhibited robustness to changes in lighting, viewpoint, distance, and time. The research findings would provide technical references for the design of visual systems for intelligent agricultural machinery. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Greenhouses

Controlled terms: Agricultural robots? - ?Benchmarking

Uncontrolled terms: Feature aggregation? - ?Images processing? - ?Lighting conditions? - ?Local feature? - ?Multilayers perceptrons? - ?Recognition methods? - ?Scene recognition? - ?Solar greenhouse? - ?Vision transformer? - ?Visual systems

Classification code: 731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.7 Farm Buildings and Other Structures? - ?913.3 Quality Assurance and Control

Numerical data indexing: Percentage 2.10E+01%, Percentage 4.30E+01%, Percentage 4.70E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.045

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

43. Design and Experiment of Soil Detection Robot for Wide-rowing Fruit and Vegetable Planung Environments

Accession number: 20251018008458

Title of translation: 宽行距果蔬种植环境土壤检测机器人设计与试验

Authors: Zhang, Rihong (1); Chen, Dezhao (1); Wang, Zhenhao (1); She, Zipeng (2); Wang, Baoe (2)

Author affiliation: (1) College of Mechanieal and Electrical Engineering, Zlumgkai University of Agriculture and Engineering, Guangzhou; 510225, China; (2) College of Resources and Environment, Zhongkai University of Agriculture and Engineering, Guangzhou; 510225, China

Corresponding author: Wang, Baoe(baoewang@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 217-228

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to aohieve effioient autonomous Operation for soil parameter detection in the wide-row planting environment of fruits and vegetables, the structure and eontrol circuit of the soil drilling module and the detection sensor movement module for the multi-parameter soil detection robot were designed according to the requirements of automated soil testing tasks, and it was equipped with a visual navigation module. The visual navigation eontrol module used the lightweight segmentation model DS - U Net for path recognition, extracted the region of interest from the segmented path, acquired the left and right boundary points to calculate the middle navigation point, and then fit the navigation line by using the least Squares method. Combined with the real-time acquisition of the robot’s heading angle, the PID algorithm was applied for aecurate Walking navigation eontrol. Experiments showed that the DS _ U Net model had only 6.5 X 10 parameters, with a recognition frame rate of 63. 17 frames per second, an average aecuraey of 94. 68%, and an Fl score of 89. 87%, demonstrating good real-time Performance and aecuraey. With no initial position deviation, the average error at different speeds was no more than 0. 074 m, with a Standard error of no more than 0. 044 m. With initial position deviation, the average error was no more than 0. 085 m, with a Standard error of no more than 0. 088 m. The soil drilling and detection sensor movement module operated stably, which was capable of drilling and loosening soil at different depths and detecting parameters. The research results can provide a technical Solution for autonomous soil detection in the planting environment of fruits and vegetables. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Fruits

Controlled terms: Agricultural robots? - ?Fertilizers? - ?Image segmentation? - ?Multipurpose robots? - ?Seed? - ?Soil testing? - ?Vegetables? - ?Visual servoing

Uncontrolled terms: Average errors? - ?Detection robots? - ?Detection sensors? - ?Fruit and vegetables? - ?Plantings? - ?Soil detection? - ?Standard errors? - ?Vegetable eultivation environment? - ?Visual Navigation? - ?Wide-row spacing

Classification code: 1106.3.1 Image Processing? - ?1502.1.1.3 Soil Pollution? - ?1502.1.1.4.3 Soil Pollution Control? - ?483.1 Soils and Soil Mechanics? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals? - ?821.5 Agricultural Products

Numerical data indexing: Percentage 6.80E+01%, Percentage 8.70E+01%, Size 4.40E+01m, Size 7.40E+01m, Size 8.50E+01m, Size 8.80E+01m

DOI: 10.6041/j.issn.1000-1298.2025.02.021

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

44. Recognition Method of Flat and Ridged Crop Types in Dry Fields Based on Propriety Sensing Signals of Agricultural Robot

Accession number: 20251018009019

Title of translation: 基于农业机器人本体传感信号的旱田平作与垄作类型识别方法

Authors: Zhang, Weirong (1, 2); Chen, Xuegeng (3); Qi, Jiangtao (1, 2); Zhou, Junbo (1, 2); Wen, Haojun (3); Liu, Huili (2, 4)

Author affiliation: (1) Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun; 130022, China; (2) College of Biological and Agricultural Engineering, Jilin University, Changchun; 130022, China; (3) College of Mechanical and Electrical Engineering, Shihezi University, Shihezi; 832003, China; (4) Jilin Provincial Key Laboratory of Smart Agricultural Equipment and Technology, Changchun; 130022, China

Corresponding author: Qi, Jiangtao(qijiangtao@jlu.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 164-174

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Dryland agricultural cultivation modes include flat cropping and ridge cropping, and the terrain undulation of different cultivation modes varies greatly, so accurate crop row cultivation mode recognition is of great significance to the stability of robot travelling. A methodology for identifying the terrain of crop rows and ridges utilizing appropriate sensor signals was introduced. Initially, the inertial measurement unit (IMU) signals were collected from a quadrupedal robot navigating through the crop rows of a corn field. The velocity data from the robot’s left front leg served as supplementary information to compile a comprehensive signal dataset, encompassing the robot’s movement in both flat cropping and row cropping modes, with two distinct row heights. Subsequently, spatial information features were extracted from the signals by using convolutional neural networks (CNN), while time series features were derived through bidirectional long short-term memory (BiLSTM) networks. Additionally, self-attention (SA) was employed to capture the attention scores of the output feature information from both CNN and BiLSTM. Ultimately, the efficacy of the proposed model in distinguishing between flat and ridge crop types was validated through model comparisons and field experiments. The results indicated that the F1 score of proposed CNN - BiLSTM - SA model reached 92%, marking an improvement of 10.17, 3.51, 2.57 and 1.27 percentage points over that of the CNN, CNN - LSTM, CNN - LSTM - SA, and CNN - BiLSTM models, respectively. When the recognition model was embedded in the field robot, it achieved a 90% accuracy rate in identifying the current crop row tillage type within 1.4 s, and met the classification criteria for flat and ridge categories within 4.8 s. This performance satisfied the robot’s requirements for rapid and accurate recognition across various tillage terrains. The algorithm can provide the robot with ability to recognize crop rows under typical tillage patterns in dry fields, and the results can provide technical support for improving the field stability of quadrupedal robots in autonomous operations. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Convolutional neural networks

Controlled terms: Agricultural robots? - ?Digital storage? - ?Fertilizers? - ?Image segmentation? - ?Industrial robots? - ?Long short-term memory? - ?Multipurpose robots

Uncontrolled terms: Agricultural robot? - ?Convolutional neural network? - ?Crop rows? - ?Inertial measurements units? - ?Long? - ?Memory network? - ?Proprioceptive signal? - ?Short term memory? - ?Short-term memory network? - ?Terrain recognition

Classification code: 1101.2.1 Deep Learning? - ?1103.1 Data Storage, Equipment and Techniques? - ?1106.3.1 Image Processing? - ?1502.1.1.3 Soil Pollution? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals

Numerical data indexing: Percentage 9.00E+01%, Percentage 9.20E+01%, Time 1.40E+00s, Time 4.80E+00s

DOI: 10.6041/j.issn.1000-1298.2025.02.016

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

45. Predication Method of Fall State for Quadrupedal Robot in Field Based on IPO-VMD-GRNN

Accession number: 20251018009037

Title of translation: 基于 IPO-VMD-GRNN 的田间四足机器人摔倒状态预测方法

Authors: Zhang, Weirong (1, 2); Chen, Xuegeng (3); Qi, Jiangtao (1, 2); Zhou, Junbo (1, 2); Xiong, Yuesong (1, 2); Wang, Shuo (2, 4)

Corresponding author: Qi, Jiangtao(qijiangtao@jlu.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 175-186

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The complex operating environment of agricultural quadruped robots causes them to fall easily when walking in the field, which affects the operating efficiency of the robot, and accurate prediction of the body fall state is of great significance to the walking stability of the robot. A critical state prediction method for robot fall was proposed based on ontology sensor signal processing. Firstly, the inertial measurement sensor signals of the quadruped robot walking and falling in a corn field and the fall state of the robot during field walking simulated by Gazebo software were collected, and the signals of the robot’s normal walking, the two phases of the critical stable state of falling and the four working conditions of complete falling were classified to generate signal datasets of different body states. Secondly, a population optimisation algorithm was used to optimize the parameters of variational mode decomposition (VMD), and an improved population optimization variational mode decomposition (IPO-VMD) algorithm was proposed. And IPO algorithm was adopted to optimize the parameters of general regression neural network (GRNN), and improved population optimization general regression neural network (IPO-GRNN) was proposed. Finally, based on the above signal processing method, a fall prediction method for field operation robots based on the IPO VMD GRNN model was established, and the signals of the traverse roll and pitch attitude angle of the robot’s actual field walking were used as the model test data to verify the performance of the fall prediction model for field operation robots. The test results showed that the IPO-VMD-GRNN model outputed a total error of 0.1467, an average relative error of 0.0065, and a mean square error of 0.0003, and the extracted features were well represented; compared with the VMD-BPNN, VMD-GRNN, and PSO VMD-GRNN models, the average prediction of a successful response time was faster than the average predicted response times of 127.75 ms, 91.5 ms, and 39.5 ms. The algorithm can provide the ability to predict the critical state of robot fall when the robot walked in the field, and the results can provide technical support to improve the field passability of quadruped robots for autonomous operation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Multipurpose robots

Controlled terms: Agricultural robots? - ?Benchmarking? - ?Binary images? - ?Conformal mapping? - ?Cracking (chemical)? - ?Dynamic programming? - ?Fourier transforms? - ?Image acquisition? - ?Image analysis? - ?Image coding ? - ?Image quality? - ?Image segmentation? - ?Image texture? - ?Image thinning? - ?Industrial robots? - ?Intelligent robots? - ?Linear programming? - ?Ontology? - ?Radial basis function networks? - ?Robot Operating System ? - ?Steganography? - ?Variational mode decomposition

Uncontrolled terms: Agricultural robot? - ?Fall state prediction? - ?Improved population optimization variational mode decomposition-general regression neural network? - ?Modal decomposition? - ?Mode decomposition? - ?Optimisations? - ?Quadruped Robots? - ?Regression neural networks? - ?State prediction? - ?Variable modal decomposition

Classification code: 101.6.1 Robotic Assistants? - ?1101.2 Machine Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1106.3.1 Image Processing? - ?1106.7 Computational Linguistics? - ?1108.2 Cryptography? - ?1201.14 Geometry and Topology? - ?1201.2 Calculus and Analysis? - ?1201.3 Mathematical Transformations? - ?1201.7 Optimization Techniques? - ?716.1 Information Theory and Signal Processing? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?802.2 Chemical Reactions? - ?821.2 Agricultural Machinery and Equipment? - ?913.3 Quality Assurance and Control

Numerical data indexing: Time 1.2775E-01s, Time 3.95E-02s, Time 9.15E-02s

DOI: 10.6041/j.issn.1000-1298.2025.02.017

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

46. Effect of Soil and Water Conservation Tillage on Soil Carbon Balance in Black Soil Maize Fields

Accession number: 20251018009015

Title of translation: 水土保持耕作对黑土坡耕地玉米田土壤碳平衡的影响

Authors: Zhang, Zuohe (1, 2); Zhang, Zhongxue (2); Xue, Li (2); Zhou, Lijun (1); Li, Haoyu (1); Lü, Xianglong (1)

Author affiliation: (1) College of Agriculture and Hydraulic Engineering, Suihua University, Suihua; 152061, China; (2) School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin; 150030, China

Corresponding author: Zhang, Zhongxue(zhangzhongxue@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 454-462

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The soil erosion is severe in the cultivation of black soil slope farmland along the slope, and the soil organic carbon content is decreasing year by year. Aiming to investigate the effects of different soil and water conservation tillage techniques on soil carbon balance in maize fields on black soil slopes, a runoff plot experiment was conducted. Conventional tillage (CK) was used as a control, and six comprehensive management techniques for slope farmland were set up, including contour tillage (CT), ridge oriented field (RT), deep scarification tillage (ST), contour tillage + ridge oriented field (CR), contour tillage + deep scarification tillage (CS), and ridge oriented field + deep scarification tillage (RS). The dry matter mass and carbon content of various organs of maize harvested under different soil and water conservation tillage techniques were observed, the incremental of soil carbon storage, CO2 emission carbon, and soil erosion loss carbon in maize fields were synchronously monitored, the net primary productivity (NPP) and net soil carbon income (NSCB) of maize fields were estimated. The results showed that the total carbon sequestration of maize plants was 10 201. 93 kg/ hm2 ~12 357. 34 kg/ hm2, and the carbon sequestration of each organ in descending order was as follows: grain, stem sheath, leaf, ear axis, and root. The NPP of CT, RT, CR, CS, and RS treatments was significantly higher than that of CK treatment (P 0. 05) . Ridge oriented field, deep scarification tillage, and their combination mode would increase the total CO2 emissions of maize fields, while contour tillage and contour tillage + ridge oriented field can reduce the total CO2 emissions. The CT treatment had the highest NSCB, at 1 402. 29 kg / hm2 . The NSCB of CT, RT, CR, and CS treatments were significantly higher than those of CK treatment (P ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 43

Controlled terms: Elastin? - ?Grain (agricultural product)

Uncontrolled terms: Black soil? - ?Carbon balance? - ?CO 2 emission? - ?Conservation tillage? - ?Maize fields? - ?Net primary productivity? - ?Slope farmland? - ?Soil and water conservation? - ?Soil and water conservation tillage technique? - ?Soil carbon

Classification code: 203 Biomaterials? - ?821.5 Agricultural Products

Numerical data indexing: Mass 2.90E+01kg, Mass 3.40E+01kg, Mass 9.30E+01kg, Percentage 2.80E+01%, Percentage 5.50E+01%, Percentage 6.80E+01%, Percentage 9.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.042

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

47. Optimization of Contact Force for Spherical Fruit and Vegetable Picking Dexterous Hand Based on Minimum Force

Accession number: 20251018008493

Title of translation: 基于最小作用力的球形果蔬采摘灵巧手接触力优化

Authors: Bao, Xiulan (1, 2); Ren, Mengtao (1); Ma, Xiaojie (1); Gao, Shengtong (1); Bao, Yougang (1); Li, Shanjun (1, 2)

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

Corresponding author: Li, Shanjun(shanjunlee@mail.huaz.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 333-341

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problem that the end-effector pioking foroe is poorly matched with fruits and vegetables, resulting in a high damage rate and low versatility, a contact force optimization method for non-destructive grasping of spherical fruits and vegetables based on dexterous hands was proposed. The Separation scheme of spherical fruit and vegetable picking was analyzed, and a frictional non-destructive point contact model was established. Peaches in the picking period were taken as the research object,and the static friction coefficient and non-destructive contact parameters between the fingertip material and peaches were experimentally determined. The kinematic model and Jacobian matrix of the multi-finger dexterous hand were established based on the screw theory, the force balance constraint of fruit and vegetable picking was analyzed, and the force-position mapping model of Joint - contact point - fruit and vegetable was constructed. A grasping planning method for dexterous hands based on minimum force was proposed, and the Solution analysis was carried out using the picking of peaches as an example, and the optimal Solution of the planning model was obtained based on the NSGA - II algorithm. Field peach picking experiments showed that this method can achieve stable grasping and picking of peaches with a radius of 2. 6 ~8 cm,effectively avoiding damage to fruits and vegetables during the picking process. The average absolute contact force error between the actual measured value and the theoretical calculated value was 0. 39 N, the picking success rate was 92%, the non-destructive rate was 97. 8%, and the average picking time was 10. 3 s,which met the needs of dexterous hands for non-destructive and stable picking. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Fruits

Controlled terms: Agricultural robots? - ?End effectors? - ?Friction? - ?Jacobian matrices? - ?Spheres? - ?Vegetables

Uncontrolled terms: Contact force optimization? - ?Contact forces? - ?Dexterous hands? - ?Force optimization? - ?Force-closure? - ?Fruit and vegetables? - ?Lossless? - ?Lossless grab? - ?Picking robot? - ?Spherical fruit and vegetable ? - ?Spherical fruits

Classification code: 1201.14 Geometry and Topology? - ?1201.2 Calculus and Analysis? - ?1301.1.1 Mechanics? - ?731.5 Robotics? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products

Numerical data indexing: Force 3.90E+01N, Percentage 8.00E+00%, Percentage 9.20E+01%, Size 6.00E-02m to 8.00E-02m, Time 3.00E+00s

DOI: 10.6041/j.issn.1000-1298.2025.02.031

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

48. Wheat Moisture Content Prediction Model for Combine Harvester Based on GA BP Method

Accession number: 20251018008451

Title of translation: 基于GA-BP的联合收获机小麦含水率检测模型研究

Authors: An, Xiaofei (1, 2); Dai, Junyi (1, 2); Li, Liwei (2); Lu, Hao (2); Yin, Yanxin (2); Meng, Zhijun (2)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urunuji, 830052, China; (2) Research Center of Intelligent Equipment Technology for Agriculture, Beijing Acaclemy of Agriculture and Forestry Sciences, Beijing; 100097, China

Corresponding author: Meng, Zhijun(mengzj@nercita.org.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 325-332

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to improve the detection aoouracy and applicability of wheat moisture content deteetion device for combined harvester based on dielectric properties, the wheat moisture content prediction model was established based on GA - BP method. Focusing on three varieties of wheat, namely “Jingdong 22”, “Shumai 1958” and “Womai 33”. The measured r?nge of wheat moisture content was 8. 41% to 21. 6%, with the detection temperature ranging from 5X1 to 40°C and the bulk density ranging from 714. 44 kg/m to 777. 58 kg/rrf for wheat dielectric constant. The experiment results indicated that at constant temperature conditions, higher bulk density corresponded to a larger dielectric constant. Similarly, under consistent bulk density, the dielectric constant was increased with the increase of temperature and moisture content. To establish the relationship between dielectric constant, temperature, bulk density, and wheat moisture content, a genetic algorithm - optimized back propagation neural network (GA-BP) with 150 samples in the calibration set and 42 samples in the prediction set was established. The model, with a 3 - 5 - 1 structure, a maximum iteration of 1 000 times, and a learning error threshold of 1 X 10 ~, demonstrated high detection accuracy and stability. The verification set R, RMSE, and MAE values were 0.996, 0.241%, and 0. 189%, respectively, while the prediction set returned values were 0. 993, 0. 295%, and 0. 189%. These results underscored the model’s efficacy in providing a method for online moisture content detection in wheat of different varieties. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Genetic algorithms

Controlled terms: Backpropagation? - ?Bulk Density? - ?Dielectric properties of solids? - ?Harvesters? - ?Neural networks? - ?Prediction models

Uncontrolled terms: %moisture? - ?Back-propagation neural networks? - ?BP methods? - ?BP neural networks? - ?Bulk density? - ?Combine harvesters? - ?Constant temperature? - ?Moisture content predictions? - ?Prediction modelling? - ?Wheat moisture content

Classification code: 101.1 Biomedical Engineering? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1201.7 Optimization Techniques? - ?1301.1.2 Physical Properties of Gases, Liquids and Solids? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Linear density 4.40E+01kg/m to 7.77E+02kg/m, Mass 5.80E+01kg, Percentage 1.89E+02%, Percentage 2.41E-01%, Percentage 2.95E+02%, Percentage 4.10E+01% to 2.10E+01%, Percentage 6.00E+00%, Temperature 2.78E+02K to 3.13E+02K

DOI: 10.6041/j.issn.1000-1298.2025.02.030

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

49. Lightweight RepVIT-based Working Condition Recognition Method for Agricultural Implements

Accession number: 20251018009088

Title of translation: 基于轻量级 RepVIT 的农机具工况识别方法研究

Authors: An, Qilin (1, 2); Wang, Fengzhu (1, 2); Liu, Yangchun (1, 2); Deng, Xue (1, 2); Zhou, Liming (1, 2); Zhao, Bo (1, 2); Wei, Liguo (1, 2)

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

Corresponding author: Wei, Liguo(weilg78@126.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 187-194 and 205

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the problems of difficulty in monitoring tractor-mounted agricultural implements in complex field environments and the excessive amount of model parameters, a lightweight RepViT-based agricultural implements recognition model, tractor-mounted agricultural implements net (TMAInet), was proposed. Firstly, the self-developed agricultural machinery service platform ‘Agricultural Mechanisation Precision Operation Platform’ was used to collect the datasets of agricultural implements in six working states, and the training set was expanded to 6 627 frames by data enhancement methods such as copy-paste. Secondly, based on the RepVIT network model framework, a convolutional feed-forward module (CFF) was designed to improve the ability of fine-grained feature extraction at different scales, and an attention mechanism, ECA, was introduced to optimize the model parameter structure and simplify the feature extraction module. Finally, the model was trained by pre-training + fine-tuning (PF) migration learning method and deployed on Jetson nano edge devices. The experimental results showed that the recognition accuracy, F1 score and recall of the TMAInet model reached 99. 13%, 98. 53 and 98. 78%, respectively, by the PF migration learning method. Compared with the original RepVIT model, the recognition accuracy, F1 score and recall were improved by 1. 86 percentage points, 3. 04 percentage points and 1. 95 percentage points, respectively, and the number of parameters was reduced to 7. 3 × 106 while maintaining 73 f/s at the edge device side. TMAInet was able to accurately and efficiently monitor the common categories of agricultural implements in practical applications, and it can provide a technical reference for the development of unmanned smart farms. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 21

Main heading: Tractors (agricultural)

Controlled terms: Agricultural implements? - ?Agricultural robots

Uncontrolled terms: Condition recognition? - ?Features extraction? - ?Fine tuning? - ?Modeling parameters? - ?Percentage points? - ?Pre-training? - ?RepVIT? - ?Tractor-mounted agricultural implement? - ?Transfer learning? - ?Unmanned smart farm

Classification code: 731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment

Numerical data indexing: Percentage 1.30E+01%, Percentage 7.80E+01%

DOI: 10.6041/j.issn.1000-1298.2025.02.018

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

50. Pig Behavior Recognition Based on CBCW ? YOLO v8 Model

Accession number: 20251118025963

Title of translation: 基于 CBCW ? YOLO v8 的猪只行为识别方法研究

Authors: Tong, Zhimin (1); Xu, Tianzhe (1); Shi, Chuanmiao (1); Li, Shengzhang (1); Xie, Qiuju (2); Rong, Lihong (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao; 266109, China; (2) College of Electrical and Information, Northeast Agricultural University, Harbin; 150030, China

Corresponding author: Rong, Lihong(ronglh@qau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 2

Issue date: February 2025

Publication year: 2025

Pages: 411-419

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: With the rapid development of modern pig breeding industry, the demand for precise recognition of pig behaviors is increasing. Aiming to address the issues of diversity of pig behaviors, similarity of features, mutual occlusion and stacking, a pig behavior recognition method based on the improved YOLO v8 model was proposed. Firstly, the ConvNeXt V2 was introduced as the backbone feature extraction network to enhance the ability to extract semantic information of the detection target. Secondly, the bi-directional feature pyramid network (BiFPN) was added to the feature fusion network to enhance the feature fusion ability of the model. Thirdly, combined with the CARAFE up-sampling operator, the feature extraction ability of the model in the process of behavior recognition was further improved. Finally, the WIoUv3 was used as the loss function to optimize the detection accuracy of the model. The experimental results showed that the precision rate, recall rate, mean average precision and F1 value of the improved model reached 89. 6%, 88. 0%, 91. 9% and 88. 8%, respectively. Compared with TOOD, YOLO v7 and YOLO v8 models, the mean average precision was increased by 10. 9, 6. 3 and 3. 7 percentage points, respectively, which significantly improved the accuracy of pig behavior recognition. The ablation experiments showed that all the improvements improved the recognition performance of the model, and the ConvNeXt V2 backbone feature extraction network had the most obvious improvement effect on the model. In summary, the CBCW —YOLO v8 model demonstrated excellent overall performance in pig behavior recognition tasks and provided powerful technical support for pig health management and disease early warning. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Mammals

Controlled terms: Macroinvertebrates

Uncontrolled terms: Behaviour recognition? - ?CARAFE? - ?Feature extraction network? - ?Features extraction? - ?Features fusions? - ?Pig behavior? - ?Pig behavior recognition? - ?Targets detection? - ?Wiouv3? - ?YOLO v8

Classification code: 103 Biology? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control

Numerical data indexing: Percentage 0.00E00%, Percentage 6.00E+00%, Percentage 8.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.02.038

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village