农业机械学报

2025年第10期共收录69篇

1. Research Review on Underwater Robotics in Fisheries

Accession number: 20254519444915

Title of translation: 渔业水下机器人研究综述

Authors: Zhou, Huanyin (1); Zhu, Ruipeng (1); Liu, Jinsheng (2)

Author affiliation: (1) School of Electronic and Electrical Engineering, East China University of Technology, Nanchang; 330013, China; (2) School of Water Resources and Environmental Engineering, East China University of Technology, Nanchang; 330013, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 20-35

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: With the intelligent technologies development of underwater robots, they demonstrated significant potential capabilities in the field of aquaculture. Their applications primarily focused on three key areas; environmental monitoring, biological identification and harvesting, and cage maintenance. And the significant operational advantages of underwater robots used in aquaculture were mainly reflected in enhanced efficiency, safeguarded ecological security, and reduced operational risks. Moreover, this review summarized the current applications and developmental status of underwater robots in aquaculture. To address the bottleneck of endurance capability of underwater robotic in aquaculture operations, the impact of thruster configuration and mechanical structure on energy consumption was analyzed. Based on this analysis, relevant technological pathways for reducing energy consumption were proposed. It has been noted that underwater robots encountered several challenges in the context of fishery aquaculture development, including limited capability in perception information fusion, insufficient adaptability to environmental conditions, and inadequate fluid dynamic design. To address these issues, current research suggested that future advancements in fishery underwater robots should focus on the integration of interdisciplinary technologies, promoting the development of bionic and intelligent systems. This approach aimed to optimize perception fusion systems, intelligent algorithms, and structural design, thereby supporting the transformation of fishery aquaculture toward intelligent and precision-based production. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 109

Main heading: Machine design

Controlled terms: Biomimetics? - ?Environmental technology? - ?Fisheries? - ?Fluid dynamics? - ?Information fusion? - ?Intelligent robots? - ?Structural design

Uncontrolled terms: ‘current? - ?Biological identification? - ?Biological identification and harvesting? - ?Cage maintenance? - ?Environmental Monitoring? - ?Intelligent technology? - ?Research review? - ?Technology development? - ?Underwater robotics? - ?Underwater robots

Classification code: 101.6.1 Robotic Assistants? - ?101.7 Biotechnology? - ?103 Biology? - ?301.1 Fluid Flow? - ?408 Structural Design? - ?471.5 Sea as Source of Minerals and Food? - ?601 Mechanical Design? - ?731.6 Robot Applications? - ?822 Food Technology? - ?903.1 Information Sources and Analysis? - ?904 Design? - ?1502 Environmental Engineering

DOI: 10.6041/j.issn.1000-1298.2025.10.002

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

2. Real-time Detection Model for Grazing Sheep in Grassland Based on UAV Imagery and MLL-YOLO v10s

Accession number: 20254419440384

Title of translation: 基于无人机图像和MLL-YOLO vlOs的草原放牧羊只实时检测模型

Authors: Zhang, Dongyan (1, 2); Ye, Jiawei (1, 2); Guo, Yangyang (3); Hu, Gensheng (3); Li, Weifeng (1, 2); Tang, Jinglei (1); Han, Dong (1, 2)

Author affiliation: (1) Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (3) National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei; 230601, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 575-584

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to meet the demand of herdsmen in Inner Mongolia grassland pastures for accurate real-time monitoring and management of large flocks of free-range sheep, a high-precision and lightweight real-time unmanned aerial vehicle ( UAV ) remote sensing target detection model named MLL - YOLO v10s ( MobileNetV4 LSKA LSCD - YOLO v10s) was proposed. This model enabled real-time detection of individual sheep in large flocks from the high-altitude perspective of UAVs. To address the challenges of difficult sheep detection and poor real-time performance caused by densely packed and mutually occluded sheep, the following improvements were made based on the YOLO (you only look once) v10 model. MobileNetV4 was employed as the backbone network to reduce the number of model parameters and enhance computational efficiency. The large separable kernel attention (LSKA) module was introduced to strengthen the model’s ability to capture features of small targets. A lightweight shared convolutional detection head ( LSCD ) was designed to reduce computational redundancy through weight sharing and improve the computational efficiency of the model. Compared with the YOLO series, faster regions with convolutional neural networks ( Faster R - CNN), and other classic network models, the improved MLL - YOLO v10s model achieved a mean average precision ( mAP) of 93. 6% on the test set, which was 3. 4 percentage points higher than that of the baseline model. It had an average frame rate of 135 f/s and only 1. 268 × 107 parameters. In densely occluded scenarios, the false-negative rate was significantly reduced. The model’s size and computational requirements were superior to those of mainstream single/dual-stage target detection algorithms. The proposed MLL - YOLO v10s model demonstrated stronger robustness in detecting densely aggregated and partially occluded sheep in UAV aerial photography scenarios. It also had obvious advantages in terms of the number of parameters and computational requirements. This model provided support for the combined application of edge computing devices and UAVs, offering an effective real-time detection method for UAV-based sheep flock inspections in natural pastures. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Unmanned aerial vehicles (UAV)

Controlled terms: Aerial photography? - ?Agriculture? - ?Aircraft detection? - ?Antennas? - ?Computational efficiency? - ?Convolution? - ?Convolutional neural networks? - ?Edge computing? - ?Image enhancement? - ?Object detection ? - ?Object recognition? - ?Remote sensing? - ?Vehicle detection

Uncontrolled terms: Aerial vehicle? - ?Computational requirements? - ?Detection models? - ?Lightweight? - ?Mobilenetv4? - ?Real-time detection? - ?Sheep detection? - ?Small objects? - ?Unmanned aerial vehicle? - ?You only look once v10

Classification code: 435.2 Tracking and Positioning? - ?652.1 Aircraft? - ?716.1 Information Theory and Signal Processing? - ?716.2 Radar Systems and Equipment? - ?716.5.1 Antennas? - ?731.1 Control Systems? - ?742.1 Photography? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?1101.2.1 Deep Learning? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1105 Computer Networks? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 6.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.052

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

3. Design and Experiment of Pneumatic-Mechanical Combined Flattening Device for Cigar Tobacco Leaves

Accession number: 20254419440396

Title of translation: 气力机械组合式雪茄烟叶展平装置设计与试验

Authors: Yang, Heng (1); Liao, Qingxi (1, 2); Deng, Chengnuo (1); Liu, Kaiwen (1); Yang, Chunlei (3); Yang, Jinpeng (3); Zhang, Qingsong (1, 2); Du, Wenbin (1)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 397-409

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of severe curling and wrinkling of cigar tobacco leaves after rehydration by air-drying, as well as the high labor intensity and cost of manual flattening, and the lack of mechanized leaf spreading technology and equipment, etc. The tobacco leaf flattening process scheme of sequentially feeding adsorption humidification, intermittent conveying flattening, flattening and setting and blanking was proposed. At the same time, based on the characteristics of cigar-coated tobacco leaves after air-curing and moisture regaining, a pneumatic mechanical combined cigar leaf flattening device was designed. The tensile strength of various parts of cigar wrapper tobacco leaves was tested in both horizontal and vertical directions. It was clarified that the method of spreading leaves involves horizontal stretching for flattening and vertical pressing for shaping. The air flow field of the suction chamber was simulated based on CFD. The simulation orthogonal test determined that the negative pressure belt of suction hole was determined to be 6 mm, the depth of suction hole was 4 mm, the hole spacing of suction hole was 25 mm. The structural and operational parameters of key components such as the spreading mechanism and pressing mechanism were analyzed and defined. A mechanical analysis of the leaf expansion process was conducted, leading to the development of a mechanical model for the leaf pressing process. By taking the electric push rod speed, the stepper motor speed and the centrifugal fan speed as experimental factors, taking the leaf surface leveling rate and leaf breakage rate as evaluation indexes, the results of the orthogonal experiment indicated that the optimal parameter combination was obtained as electric push rod speed of 22 mm/s, stepper motor speed of 201 r/min and centrifugal fan speed of 2 275 r/min. At this time, the validation test was carried out. The leaf spreading rate and leaf damage rate of the leaf spreading device were 85. 72% and 5. 76%, respectively. Compared with the artificial leaf spreading, the leaf width and leaf area increase rate were 93.82% and 92.76%, respectively, meeting the requirements for cigar leaf flattening. The research results can provide reference for the mechanization and automation of cigar tobacco leaf flattening devices. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Tobacco

Controlled terms: Centrifugation? - ?Conveying? - ?Curing? - ?Fans? - ?Moisture? - ?Pneumatics? - ?Wages

Uncontrolled terms: CFD simulations? - ?Cigar tobacco leaf? - ?Flattening device? - ?Mechanical? - ?Motor speed? - ?Pneumatic suction? - ?Pressung? - ?Push rods? - ?Stepper motor? - ?Tobacco leaf

Classification code: 609.3 Blowers and Fans? - ?692.1 Conveyors? - ?802.2 Chemical Reactions? - ?802.3 Chemical Operations? - ?821.5 Agricultural Products? - ?912.3 Personnel? - ?1401.3 Pneumatics, Equipment and Machinery

Numerical data indexing: Angular velocity 3.3567E+00rad/s, Angular velocity 4.5925E+00rad/s, Percentage 7.20E+01%, Percentage 7.60E+01%, Percentage 9.276E+01%, Percentage 9.382E+01%, Size 2.50E-02m, Size 4.00E-03m, Size 6.00E-03m, Velocity 2.20E-02m/s

DOI: 10.6041/j.issn.1000-1298.2025.10.034

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

4. Key Organ Segmentation Method for Wheat at Grain Filling Stage Based on CA SegNet Network

Accession number: 20254519444913

Title of translation: 基于 CA-SegNet 网络的灌浆期小麦关键器官分割方法

Authors: Tian, Shijie (1, 2); Yan, Jiaxin (1); Qin, Weijie (1); Bai, Shaoxing (1); Hong, Minke (3, 4); Sun, Zhangtong (1, 3); Hu, Jin (1, 3)

Author affiliation: (1) College of Information Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Shaanxi, Yangling; 712100, China; (3) Key Laboratory of Agricultural Internet oj Things, Ministry of Agriculture and Rural Affairs, Shaanxi, Yangling; 712100, China; (4) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 520-529

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The grain filling stage is a critical period affecting wheat yield and quality, where accurate identification of flag leaves and spikes holds significant importance for phenotypic analysis and high-yield wheat selection. However, wheat point clouds during this stage face challenges such as sparse data, dense organ distribution, and severe occlusions, posing substantial difficulties for semantic segmentation of key organs. To address these issues, a point cloud data augmentation strategy and a key organ segmentation network, termed class-aware segmentation network (CA — SegNet) were proposed. Firstly, dense point cloud data were acquired through 3D reconstruction of 80 wheat plants using the 3D Gaussian splatting (3DGS) algorithm. Secondly, to enhance sample diversity while preserving critical geometric structures, a dynamic voxel farthest point sampling (DVFPS) method was developed, which integrated dynamic voxel partitioning with farthest point sampling to achieve a 10-fold dataset expansion and standardized point cloud size (8 192 points). Finally, the improved CA — SegNet was proposed, which introduced a class-aware feature extraction (CAFE) module in the encoder stage. This module incorporated dual-stage class attention (DCA) to fuse spatial structural information, enabling prioritized focus on semantically significant central points. Additionally, an enhanced loss function combining classification accuracy, spatial consistency, and feature constraints was designed to further optimize segmentation performance. Experimental results demonstrated that CA — SegNet achieved superior performance with mloU, precision, recall, and Fl-score of 62.28%, 76.41%, 72.63%, and 74.47%, respectively, outperforming methods such as PointNet + + and PlantNet. Ablation studies confirmed the effectiveness of DVFPS, CAFE, and the improved loss function in enhancing model performance. The spike volume calculated based on the segmentation results achieved an R value of 0. 85 and an RMSE of 79. 16 cm, further validating the effectiveness of the method in phenotypic analysis. The research significantly improved the segmentation accuracy of key organs in wheat point clouds, providing a reliable technical approach for wheat phenotypic detection and precision breeding in smart agriculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Semantics

Controlled terms: 3D reconstruction? - ?Feature extraction? - ?Filling? - ?Grain (agricultural product)? - ?Latent semantic analysis? - ?Plant diseases? - ?Plants (botany)? - ?Semantic Segmentation

Uncontrolled terms: 3D point cloud? - ?Data augmentation? - ?Enhanced loss function? - ?Filling stage? - ?Grain filling? - ?Loss functions? - ?Plant phenotype? - ?Point cloud data? - ?Point cloud data augmentation? - ?Semantic segmentation ? - ?Wheat at grain filling stage

Classification code: 103 Biology? - ?691.2 Materials Handling Methods? - ?821.5 Agricultural Products? - ?903.2 Information Dissemination? - ?1101.2 Machine Learning? - ?1106.7 Computational Linguistics? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 6.228E+01%, Percentage 7.263E+01%, Percentage 7.447E+01%, Percentage 7.641E+01%, Size 1.60E-01m

DOI: 10.6041/j.issn.1000-1298.2025.10.046

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

5. Optimization of Impeller for Bidirectional Axial Flow Pumps with Different Reversible Hydrofoils

Accession number: 20254419440390

Title of translation: 不同可逆翼型双向轴流泵叶轮优化

Authors: Sun, Zhuangzhuang (1); Zhu, Yadong (1, 2); Chen, Jiaqi (3); Lü, Ning (1); Tang, Fangping (2); Chen, Songshan (3)

Author affiliation: (1) School of Mechanical Engineering, Yangzhou Polytechnic University, Yangzhou; 225100, China; (2) College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou; 225100, China; (3) College of Electrical and Energy Power Engineering, Yangzhou University, Yangzhou; 225100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 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: Aiming to enhance the capability of cities to cope with complex climate conditions and satisfy the need for bi-directional pumping, an optimization study was conducted on widely used bi-directional axial flow pumps. The aim was to improve their hydraulic performance and reduce energy consumption, initially, based on the S-hydrofoil with concave-convex and convex-concave profiles relative to the working face, as well as flat plate hydrofoils, a combined optimization strategy was employed in conjunction with numerical simulation to optimize the impeller design of a bi-directional pump with a specific speed of 1 200. The optimized designs of impellers using these three types of hydrofoils were compared, and the optimal hydrofoil configuration was selected for further full-parameter optimization. The research findings indicated that the impeller designed by using the concave-convex S-hydrofoil exhibited the best overall performance, with the most stable internal flow pattern, followed by the flat plate hydrofoil, and the convex-concave S-hydrofoil performed the worst. The concave-convex S-hydrofoil can be considered as the preferred option for bi-directional pump design. In contrast, the convex-concave S-hydrofoil impeller required a relatively large blade area radio due to cavitation performance constraints, resulting in significantly lower impeller efficiency compared with other designs, making it unsuitable for hydraulic machinery applications. Additionally, appropriately reducing the camber of the hydrofoil at the shroud and hub while increasing it in the middle of the blade favors enhanced bi-directional pump performance. Following blade parameter optimization, the impeller efficiency was increased by 1. 7, 1.8, and 5. 2 percentage points at 0. 8, 1.0, and 1. 2 times the forward design flow conditions, respectively. The research result can provide valuable insights for the optimization design of reversible rotating machinery. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Impellers

Controlled terms: Axial flow? - ?Cavitation? - ?Computational fluid dynamics? - ?Energy utilization? - ?Geometry? - ?Hydraulic machinery? - ?Hydrofoils? - ?Linear programming? - ?Machine design? - ?Pumps

Uncontrolled terms: Axial flow pump? - ?Bidirectional axial flow pump? - ?Impeller optimized design? - ?Lagrangian? - ?Latin hypercube designs? - ?Non-linear programming? - ?Non-linear programming by quadratic lagrangian? - ?Optimized designs? - ?Optimized latin hypercube design? - ?Reversible hydrofoil

Classification code: 301.1 Fluid Flow? - ?301.1.1 Liquid Dynamics? - ?301.1.4 Computational Fluid Dynamics? - ?601 Mechanical Design? - ?601.2 Machine Components? - ?609.2 Pumps? - ?674 Small Craft and Other Marine Craft? - ?904 Design? - ?1009.2 Energy Consumption? - ?1201.7 Optimization Techniques? - ?1201.14 Geometry and Topology? - ?1401.2 Hydraulic Equipment and Machinery

DOI: 10.6041/j.issn.1000-1298.2025.10.036

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

6. Effects of Nitrogen Utilization Differences among Rice Varieties on Yield

Accession number: 20254419440398

Title of translation: 不同水稻品种氮素利用差异对产量的影响

Authors: Qi, Zhijuan (1, 2); Li, Sirui (1, 2); Du, Sicheng (1, 2); Guan, Sheng (1, 2); Xu, Dan (2, 3); Zhang, Guangbin (4); Ma, Jing (4); Yan, Xiaoyuan (4)

Author affiliation: (1) School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin; 150030, China; (2) Key Laboratory of Efficient Use of Agricultural Water Resources, Ministry of Agriculture and Rural Affairs, Northeast Agricultural University, Harbin; 150030, China; (3) School of Arts and Sciences, Northeast Agricultural University, Harbin; 150030, China; (4) Institute of Soil Science, Chinese Academy of Sciences, Nanjing; 211135, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 684-692

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to investigate the effects of varietal differences in NUE on rice yield, a two-year fixed-location field experiment was conducted on five major local cultivars. The results demonstrated that Longjing 31 achieved the highest average yield of 9. 67 t/hm2, followed by Suijing 18 ( 9. 10 t/hm2), Longqing 32 (9. 06 t/hm2), Longqing 31 ( 8. 34 t/hm2), and Longjing 20 (7. 20 t/hm2). Correlation and path analysis indicated that seven indicators showed a highly significant correlation with yield (p 2 was 0. 221). Moreover, the nitrogen transport amount in the other four cultivars was significantly higher (p ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Efficiency

Controlled terms: Agribusiness? - ?Grain (agricultural product)? - ?Nitrogen? - ?Plants (botany)? - ?Seed

Uncontrolled terms: Grain yield? - ?Leaf Area Index? - ?Nitrogen accumulation? - ?Nitrogen transport? - ?Nitrogen utilization? - ?Nitrogen-use efficiency? - ?Path analysis? - ?Path coefficients? - ?Rice variety? - ?Yield components

Classification code: 103 Biology? - ?804 Chemical Products? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?913.1 Production Engineering

Numerical data indexing: Percentage 2.121E+01% to 9.405E+01%, Percentage 3.60E+01%, Percentage 4.50E+01% to 4.00E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.062

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

7. Towards Smart Fishery: Application Status and Prospects of Digital-intelligent Technology System in Unmanned Aquaculture Factories

Accession number: 20254519444924

Title of translation: 智慧渔业无人养殖工厂数智技术体系研究综述

Authors: Ma, Zhihong (1); Zhao, Tianhao (1); Chen, Yuze (1); He, Yuhang (1); Hu, Qingsong (2); Fang, Hui (3); Zheng, Hanfeng (3); Liu, Ying (1)

Author affiliation: (1) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou; 310058, China; (2) College of Engineering Science and Technology, Shanghai Ocean University, Shanghai; 201306, China; (3) East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai; 200090, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 1-19

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Smart fishery unmanned aquaculture factories, as a transformative production model centered on digital-intelligent technologies, breaking through the bottlenecks of traditional aquaculture, such as over-reliance on manual experience and extensive management by enabling full-process unmanned management. The current applications and future prospects of core digital-intelligent technologies in smart fishery unmanned aquaculture factories were systematically reviewed. It emphasized how these innovations transition aquaculture paradigms from traditional experience-driven practices to data-intelligent frameworks. The system was designed around a closed-loop control architecture that integrated biological state monitoring, environmental regulation, resource management, and production decision-making, operating through a “ monitoring-analysis-decision-execution” mechanism. The key technologies were innovatively explored; intelligent precision feeding achieved demand-driven supply via multi-modal fusion of computer vision, acoustic sensing, and adaptive algorithms; water quality monitoring realized multiparameter synergy through integrated electrochemical, spectroscopic, and machine vision techniques; disease prevention formed an early warning chain combining phenotypic recognition, behavioral analysis, and molecular detection; growth models evolved from static statistical fitting to dynamic integration of bioenergetics and machine learning; intelligent equipment systems built a collaborative network of sensing, decision-making, and execution; and automatic processing realized precision via robotic operations and non-invasive quality detection. Future research should prioritize three key directions; hybrid modeling integrating biological mechanisms and data-driven approaches to enhance interpretability and prediction robustness; cloud-edge collaborative reasoning to boost real-time decision-making; and interdisciplinary integration of flexible electronics, bionic materials, and ecological engineering. These efforts would drive the evolution from automation to adaptive intelligent regulation and lay a technical foundation for the green and high-quality development of aquaculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 57

Main heading: Flexible electronics

Controlled terms: Agricultural machinery? - ?Automation? - ?Decision making? - ?Environmental management? - ?Environmental regulations? - ?Fisheries? - ?Learning algorithms? - ?Machine learning

Uncontrolled terms: Application prospect? - ?Application status? - ?Decisions makings? - ?Digital-intelligent technology? - ?Fishery equipment? - ?Intelligent technology? - ?Smart fishery? - ?Status and prospect? - ?Technology system? - ?Unmanned aquaculture factory

Classification code: 471.5 Sea as Source of Minerals and Food? - ?715 Electronic Equipment, General Purpose and Industrial? - ?731 Automatic Control Principles and Applications? - ?821.2 Agricultural Machinery and Equipment? - ?822 Food Technology? - ?912.2 Management? - ?1101.2 Machine Learning? - ?1501.1 Sustainable Development? - ?1502.1 Environmental Impact and Protection

DOI: 10.6041/j.issn.1000-1298.2025.10.001

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

8. Sugarcane Leaf Disease Recognition Algorithm Based on Improved YOLO v8n

Accession number: 20254419440402

Title of translation: 基于改进YOLO v8n的甘蔗叶片病害识别算法

Authors: Liang, Sijia (1); Li, Lei (2); Gu, Yue (1); Zhou, Yansuo (3, 4); Li, Yu (5); Yu, Jiajia (1)

Author affiliation: (1) School of Automation, Zhejiang Polytechnic University of Mechanical and Electrical Engineering, Hangzhou; 310053, China; (2) School of information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou; 310018, China; (3) Zhejiang Institute of Industry and Information Technology, Hangzhou; 310003, China; (4) School of Mechanical Engineering, Tianjin University, Tianjin; 300072, China; (5) Zhejiang Society for Agricultural Machinery, Hangzhou; 310003, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 567-574

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of few algorithms for detecting sugarcane leaf disease, large model parameters and low recognition accuracy, a lightweight sugarcane leaf disease recognition algorithm based on YOLO v8n was designed. The algorithm took five common sugarcane leaf diseases as the research object. The standard convolution module and C2f module in the network structure were replaced by Ghost convolution module and C3Ghost module, respectively, to reduce the amount of model parameters and calculation. The EMA attention mechanism was added to the backbone network to enhance the multi-scale feature extraction and fusion of the algorithm. A small target detection layer was added to the model structure to enhance the detection effect of small targets in the data. The results showed that compared with the original YOLO v8n model, the precision, recall and mean average precision of the improved model in the test set were 90. 4%, 95. 7% and 95. 3%, respectively, which were 2.8 percentage points, 11.0 percentage points and 4. 3 percentage points higher than that of the original model. The parameters and model size were reduced by 46.5% and 41.3%, respectively, and the detection effect was significantly better than that of other lightweight detection algorithms, which can provide reference for the real-time detection of sugarcane leaf disease. This study can serve as an effective method for early disease monitoring of sugarcance to provide guidance for precision prevention and control, and can offer data support for sugarcane cultivation and management decisions, thereby effectively improving the efficiency and benefits of sugarcane cultivation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Deep learning

Controlled terms: Convolution? - ?Cultivation? - ?Disease control? - ?Model structures? - ?Molasses? - ?Signal detection? - ?Target tracking

Uncontrolled terms: Attention mechanisms? - ?Deep learning? - ?Leaf disease? - ?Lightweighting? - ?Modeling parameters? - ?Percentage points? - ?Recognition algorithm? - ?Sugarcane leaf disease? - ?Targets detection? - ?YOLO v8n

Classification code: 102.1.2 Health Science? - ?435.2 Tracking and Positioning? - ?716.1 Information Theory and Signal Processing? - ?821.4 Agricultural Methods? - ?822.3 Food Products? - ?1101.2.1 Deep Learning? - ?1201.12 Modeling and Simulation

Numerical data indexing: Percentage 3.00E+00%, Percentage 4.00E+00%, Percentage 4.13E+01%, Percentage 4.65E+01%, Percentage 7.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.051

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

9. Design and Experiment of Tobacco Stalk Harvesting Machine Based on Reverse Alternating Rotary Excavation

Accession number: 20254419440386

Title of translation: 基于逆向交替旋掘的烟秆拔除收集作业机设计与试验

Authors: Li, Lianhao (1); Han, Shuo (1); Yang, Xiaomi (1); Ma, Guozhen (1); Liu, Yuan (2); Zhao, Dayong (3)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou; 450002, China; (2) Henan Tobacco Company Nanyang City Company, Nanyang; 473061, China; (3) Agricultural Machinery Research Institute, Harbin Academy of Agricultural Sciences, Harbin; 150029, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 410-419

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of low mechanization, inefficiency, and functional limitations in tobacco stalk removal in China, an integrated whole-stalk harvesting and collecting machine was developed based on the reverse alternating rotary excavation principle, enabling continuous operations, including stalk extraction, conveying, soil separation, and pile formation in a single workflow. Guided by agronomic requirements, the kinematic mechanism of reverse alternating excavation was rigorously modeled, and a prototype was designed with optimized structural parameters: the cutter roller geometry was determined through trajectory analysis and soil-stalk interaction dynamics, while the conveying chain assembly was configured with a 22. 3° inclination angle derived from theoretical force equilibrium calculations. Critical operational parameters, including the 1.3:1 speed ratio between the stalk-throwing roller and conveying chain, were calibrated to satisfy material transfer thresholds. The discharge gate of the collection box was structurally redesigned to ensure parallel alignment during full opening, enhancing operational reliability. Field validation tests demonstrated a 96. 7% stalk extraction rate and a 3. 3% residue retention rate, meeting both design specifications and practical agricultural performance criteria. The integration of multi-functional modules, precision kinematics, and agronomic compatibility positioned this system as a robust solution for sustainable tobacco field management, bridging the gap between mechanical innovation and crop-specific operational demands. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 14

Main heading: Kinematics

Controlled terms: Agricultural machinery? - ?Agronomy? - ?Chains? - ?Conveying? - ?Excavation? - ?Extraction? - ?Rollers (machine components)? - ?Soil testing? - ?Tobacco

Uncontrolled terms: Collection work? - ?Continuous operation? - ?Field test? - ?Functionals? - ?Harvesting machines? - ?Integrated whole? - ?Kinematic Analysis? - ?Mechanisation? - ?Stalk removal? - ?Tobacco stalks

Classification code: 405.2 Construction Methods? - ?408.1 Structural Members and Shapes? - ?483.1 Soils and Soil Mechanics? - ?601.2 Machine Components? - ?602.1 Mechanical Drives? - ?692.1 Conveyors? - ?802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?1301.1.1 Mechanics? - ?1502.1.1.4.3 Soil Pollution Control

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

DOI: 10.6041/j.issn.1000-1298.2025.10.035

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

10. Synergistic Inversion of Citrus Leaf Water Content and Chlorophyll Content Based on PSO-ELM and Optimized Sensitive Vegetation Indices

Accession number: 20254419440401

Title of translation: 基于PSO-ELM与敏感植被指数优选的柑橘叶片含水率-叶绿素含量协同反演

Authors: Hao, Kun (1, 2); Zhang, Weiqi (1, 2); Zhong, Yun (1, 2); Sun, Aihua (1, 2); Zhu, Shijiang (1, 2); Zhang, Yanqun (3); Wang, Yalin (1, 2); Li, Hu (1, 2)

Author affiliation: (1) Hubei Key Laboratory of Hydropower Engineering Construction and Management, China Three Gorges University, Yichang; 443002, China; (2) College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang; 443002, China; (3) Jiangxi Gannan Branch, Dongguan Water Resources Survey and Design Institute Co., Ltd., Nanchang; 330001, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 470-478 and 511

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The moisture content and chlorophyll levels of leaves are important physiological indicators reflecting the healthy growth and development of crops. To enable continuous, rapid, precise, non-destructive, and large-scale acquisition of citrus leaf moisture content and chlorophyll content, a synergistic inversion method that combined sensitive vegetation index optimization and particle swarm optimization-based extreme learning machine ( PSO - ELM ) was proposed using drone multispectral remote sensing technology. Taking citrus in western Hubei as the study object, multispectral drone imagery throughout the entire growth period and simultaneous ground measurement data were used. Five vegetation indices strongly correlated with leaf moisture content and chlorophyll content were selected to construct sensitive vegetation index sets. Modeling comparisons were made by using partial least squares regression (PLS), extreme learning machine ( ELM ), PSO - ELM, and the PSO - ELM synergistic inversion algorithm. The results showed that the PSO - ELM-based synergistic inversion, driven by sensitive vegetation indices, performed the best, improving the inversion accuracy of leaf moisture content and chlorophyll content by 15. 16% and 53.78%, respectively, compared with the traditional PLS model. The accuracy was also enhanced by 20. 80% and 25. 84%, respectively, compared with the ELM model. Additionally, compared with PSO - ELM alone, the synergistic inversion showed an improvement of 6. 18% for leaf moisture content and 4. 02% for chlorophyll content. The PSO - ELM-based synergistic inversion can achieve simultaneous estimation of citrus leaf moisture content and chlorophyll content, with R-values of 0. 790 for leaf moisture content and 0. 672 for chlorophyll content in the validation set. The research result can provide a theoretical basis for the application of drone multispectral remote sensing in the physiological monitoring of fruit trees. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Chlorophyll

Controlled terms: Citrus fruits? - ?Drones? - ?Forestry? - ?Knowledge acquisition? - ?Learning systems? - ?Least squares approximations? - ?Machine learning? - ?Moisture determination? - ?Orchards? - ?Physiology ? - ?Remote sensing? - ?Technology transfer? - ?Vegetation? - ?Water content

Uncontrolled terms: Chlorophyll contents? - ?Citrus? - ?Inversion? - ?Leaf water content? - ?Learning machines? - ?Multi-spectral? - ?Particle swarm? - ?Particle swarm optimization-based extreme learning machine? - ?Swarm optimization? - ?UAV-based multispectral

Classification code: 103 Biology? - ?652.1.2 Military Aircraft? - ?731.1 Control Systems? - ?804.1 Organic Compounds? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.1 Woodlands and Forestry? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?941.6 Moisture Measurements? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1201.7 Optimization Techniques

Numerical data indexing: Percentage 1.60E+01%, Percentage 1.80E+01%, Percentage 2.00E+00%, Percentage 5.378E+01%, Percentage 8.00E+01%, Percentage 8.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.041

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

11. Degradation Risk of Cultivated Land Ecosystems in Major Grain Producing Areas Based on Measurement-Differentiation-Mechanism-Control Framework

Accession number: 20254419440389

Title of translation: 基于测度-分异-机制-管控框架的粮食主产区耕地生态系统退化风险研究

Authors: Guo, Xinxin (1); Sun, Jiarong (1); Sun, Zhe (1); Cai, Yajun (1); Ma, Tongtong (1); Du, Guoming (1)

Author affiliation: (1) College of Public Administration and Law, Northeast Agricultural University, Harbin; 150030, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 635-647

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Comprehensively understanding the level, distribution characteristics, and driving mechanisms of cultivated land ecosystem degradation risks in major grain producing areas can provide valuable references for the prevention and control of regional cultivated land ecosystem degradation risks. Taking Heilongjiang Province as an example, based on the measurement - differentiation - mechanism - control framework, the negative value and net value of cultivated land ecosystem services were introduced to construct a cultivated land ecosystem degradation risk index. Using spatial autocorrelation and geographical detector methods, the spatial differentiation characteristics and influencing mechanisms of cultivated land ecosystem degradation risks in Heilongjiang Province in 2020 were analyzed, and control suggestions were provided. The results showed that in 2020, the areas with moderate and higher degradation risk in the cultivated land ecosystem of Heilongjiang Province accounted for 62. 03% of the total number of counties. The average value of the degradation risk index of the regional cultivated land ecosystem in Heilongjiang Province was 22. 25%, which was at a moderate degradation level. The degradation risk of the cultivated land ecosystem showed obvious heterogeneity and aggregation in space. The areas with moderate and higher degradation risk were mainly distributed in the western and southwestern part of the study area, while the areas with low degradation risk and relatively low degradation risk were mainly distributed in the north-south axial zone (excluding the central region) and the eastern region of the study area, and presented the aggregation characteristics mainly characterized by low - low and high - high agglomeration. Socio-economic factors such as population density, road network density, urbanization rate, per capita GDP, agricultural machinery input intensity, and irrigation index were the main controlling factors for the degradation of the cultivated land ecosystem in Heilongjiang Province. The interactions between cultivated land input intensity and population economic indicators, as well as the interactions among the key indicators of the intensity of cultivated land input and among population-economic indicators, would significantly affect the level of degradation risk. Measures such as zoning control to reduce source pressure, full-process monitoring, and implementing degradation remediation should be taken to control the entire lifecycle of cultivated land quality. The research results can provide theoretical and practical references for measuring and curbing cultivated land ecosystem degradation risks in Heilongjiang Province and other major grain producing areas. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Risk assessment

Controlled terms: Agglomeration? - ?Agricultural economics? - ?Agricultural robots? - ?Autocorrelation? - ?Cultivation? - ?Economics? - ?Ecosystems? - ?Grain (agricultural product)? - ?Irrigation? - ?Population distribution ? - ?Population statistics? - ?Process control? - ?Zoning

Uncontrolled terms: Cultivated land ecosystem? - ?Cultivated lands? - ?Degradation risk? - ?Ecosystem degradation? - ?Ecosystem service values? - ?Heilongjiang? - ?Heilongjiang province? - ?Major grain producing area? - ?Mechanism control? - ?Producing areas

Classification code: 403 Urban and Regional Planning and Development? - ?405.3 Surveying? - ?731 Automatic Control Principles and Applications? - ?731.6 Robot Applications? - ?802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?911.2 Industrial Economics? - ?913.3 Quality Assurance and Control? - ?914.1 Accidents and Accident Prevention? - ?971 Social Sciences? - ?1108 Security and Privacy? - ?1201 Mathematics? - ?1202.2 Mathematical Statistics? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 2.50E+01%, Percentage 3.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.058

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

12. Design and Test of Full Hydraulic Multi-wheel Steering Control System for High Ground Clearance Sprayer

Accession number: 20254419440395

Title of translation: 高地隙喷雾机全液压多轮转向控制系统设计与试验

Authors: Fu, Liangqi (1, 2); Wang, Longlong (1, 2); Mao, Enrong (1, 2); Song, Zhenghe (1, 2); Li, Zhen (1, 2); Li, Ping (3)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) State Key Laboratory of Intelligent Agricultural Power Equipment, Beijing; 100083, China; (3) College of Mechanical and Electronic Engineering, Tarim University, Alar; 843300, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 746-757

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the inconvenience and crop trampling issues caused by the large-scale of high clearance self-propelled sprayer, a full hydraulic load-sensitive steering control system that can achieve four-wheel steering and crab steering was designed. In this system, the rodless chambers of the front wheel steering hydraulic cylinders were connected in series and connected to a compensation oil circuit that can replenish or unload oil automatically. Each rear wheel steering hydraulic cylinder operated independently. Such a design met the Ackermann steering characteristics under different steering modes. Mathematical models of the steering hydraulic system, steering system control models and Matlab/ Simulink simulation models were established, and a real vehicle test platform was constructed. In the comparative experiments of four-wheel steering compensation control, the maximum Ackermann steering angle deviation of the front wheels was 1.87° with compensation control activated, whereas it reached 6.08° when deactivated. In crab steering mode, during automatic control experiments, the maximum steering angle deviation of the front wheels was 2.09°, and for the rear wheels, it was 2.71°. With compensation control intervention, the steering angle deviation of both front and rear wheels was less than the 3° threshold. Experimental results demonstrated that the designed steering control system can achieve four-wheel steering and crab steering, meeting the Ackermann steering principles under different steering modes and improving the maneuverability of high clearance self-propelled sprayers in complex operating conditions. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Four wheel steering

Controlled terms: Agriculture? - ?Automobile steering equipment? - ?Control systems? - ?Hydraulic equipment? - ?Hydraulic machinery? - ?MATLAB? - ?Process control? - ?Robotics? - ?Shellfish? - ?Simulation platform ? - ?Wheels

Uncontrolled terms: Compensation control? - ?Four-wheel steering? - ?Front wheels? - ?Ground clearance? - ?High ground clearance sprayer? - ?Hydraulic system? - ?Multi-wheel steering? - ?Steering control? - ?Steering control system? - ?Wheel steering

Classification code: 103 Biology? - ?601.2 Machine Components? - ?662.3 Automobile Components and Materials? - ?731 Automatic Control Principles and Applications? - ?731.1 Control Systems? - ?731.5 Robotics? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?913.3 Quality Assurance and Control? - ?1106.5 Computer Applications? - ?1201.5 Computational Mathematics? - ?1201.12 Modeling and Simulation? - ?1401.2 Hydraulic Equipment and Machinery

DOI: 10.6041/j.issn.1000-1298.2025.10.068

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

13. Weight Estimation Method of Beef Cattle Based on Improved YOLO v9u-pose

Accession number: 20254419440388

Title of translation: 基于改进YOLO v9u-pose的肉牛质量估算方法

Authors: Duan, Qingling (1, 2); Yang, Lisha (1, 2)

Author affiliation: (1) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Smart Breeding Technology, Ministry of Agriculture and Rural Affairs, Beijing; 100083, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 596-605

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In beef cattle farming management, the weight of beef cattle is crucial for monitoring growth, improving breeding, and controlling costs. Traditional weighing methods are not only time-consuming and labor-intensive but also prone to causing stress in the cattle. However, existing non-contact estimation methods are easily affected by posture and complex backgrounds, leading to low algorithm accuracy and poor robustness. Therefore, a beef cattle weight estimation method was proposed based on improved YOLO v9u - pose, which mainly consisted of two stages; key point detection and weight estimation. In the key point detection stage, YOLO v9u - pose was used as the baseline model, where the standard convolutions in the backbone network were replaced with omni-dimensional dynamic convolution ( ODConv ) ; DySample was employed to replace the upsampling module of the neck network; additionally, the excitation and modulation attention (EMA) was added to the RepNCSPELAN4 module connected to the detection head to improve the accuracy of the beef cattle key point detection algorithm. In the weight estimation stage, body size parameters were extracted by using depth maps and local point cloud processing methods. A beef cattle weight estimation algorithm was then constructed based on the parameters and the particle swarm optimization - eXtreme gradient boosting ( PSO - XGBoost) approach. On testing with a self-constructed dataset, the F1 score, and mean average precision ( mAP@ 0. 75 ) of the key point detection model proposed were 97. 2% and 98. 2%, respectively. The mean absolute percentage error ( MAPE ) of weight estimation method based on PSO - XGBoost was 3. 97%. Finally, the proposed cattle weight estimation model was deployed to a development board, providing technical support for intelligent beef cattle farming. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Beef

Controlled terms: Agriculture? - ?Convolution? - ?Parameter estimation? - ?Particle swarm optimization (PSO)? - ?Signal detection? - ?Statistical tests? - ?Weighing

Uncontrolled terms: Beef cattle? - ?Gradient boosting? - ?Key point detection? - ?Keypoints? - ?Particle swarm? - ?Particle swarm optimization - extreme gradient boosting? - ?Point detection? - ?Swarm optimization? - ?Weights estimation? - ?YOLO v9u-pose

Classification code: 716.1 Information Theory and Signal Processing? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?822.3 Food Products? - ?942.1.7 Special Purpose Instruments? - ?1106 Computer Software, Data Handling and Applications? - ?1201 Mathematics? - ?1201.7 Optimization Techniques? - ?1202 Statistical Methods? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 2.00E+00%, Percentage 9.70E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.054

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

14. Accuracy Compensation of Delta Robot Based on Improved Secretary Bird Optimization Algorithm

Accession number: 20254519444918

Title of translation: 基于改进蛇鹭优化算法的 Delta 机器人精度补偿研究

Authors: Chen, Jiupeng (1, 2); Wang, Ziyan (1); San, Hongjun (1, 2); He, Chaoyin (1); Zhang, Haobin (1)

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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 780-791

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the critical challenge of pose accuracy degradation in parallel robots under high-speed motion, which significantly impacted operational performance, an effective error compensation model was established as a key strategy for precision enhancement. Focusing on the high-speed Delta robot, renowned for its standard beat of 0. 33 s. An error analysis model was developed based on forward and inverse kinematics to systematically investigate the influence of various error sources on end-effector pose deviation. Building upon this analysis, an error compensation algorithm utilizing the kinematic forward solution was proposed. The improved secretary bird optimization algorithm (SBOA) was employed to optimize the error function, yielding compensation values for the rotation angles of the robot’s active arms. The average position error between the robot’s actual and ideal positions was utilized as an intuitive accuracy metric, with the average accuracy improvement value serving as the key evaluation index for compensation effectiveness. Simulation results demonstrated that the proposed compensation approach, powered by the improved SBOA algorithm, achieved a substantial 75. 44% improvement in the position accuracy of the robot’s end effector. Furthermore, physical experiments confirmed an overall average position accuracy enhancement of 68. 28% for the Delta robot. These findings validated the significant efficacy of the proposed error analysis methodology and compensation algorithm in substantially elevating the operational accuracy of high-speed Delta robots. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Error compensation

Controlled terms: Birds? - ?End effectors? - ?Error analysis? - ?Inverse kinematics? - ?Inverse problems

Uncontrolled terms: Accuracy compensation? - ?Compensation algorithm? - ?Critical challenges? - ?Delta parallel robot? - ?Delta robot? - ?Error modeling? - ?High Speed? - ?Optimization algorithms? - ?Position accuracy? - ?Secretary bird optimization algorithm

Classification code: 103 Biology? - ?731.1.1 Error Handling? - ?731.5 Robotics? - ?1201 Mathematics? - ?1301.1.1 Mechanics

Numerical data indexing: Percentage 2.80E+01%, Percentage 4.40E+01%, Time 3.30E+01s

DOI: 10.6041/j.issn.1000-1298.2025.10.071

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

15. Design and Testing of Dynamic Sliding Cutting Segmented Drive Disc Anti-blocking Device

Accession number: 20254519449343

Title of translation: 动态滑切分段式驱动圆盘防堵装置设计与试验

Authors: Bi, Jinshuo (1, 2); Lu, Caiyun (1, 2); Li, Hongwen (1, 2); Zhai, Chengkun (1, 2); Wang, Zhinan (1, 2); Gao, Shijie (1, 2)

Author affiliation: (1) College of Engineering, China Agricultural University, Beijing; 100083, China; (2) Key Laboratory of Agricultural Equipment for Conservation Tillage, Ministry of Agriculture and Rural Affairs, Beijing; 100083, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: 2025

Publication year: 2025

Pages: 291-300 and 374

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issue that cutting-type anti-blocking devices struggle to balance cutting efficiency and operation power during no-tillage seeding in the single-cropping corn region of Northeast China, a segmented driving disc anti-blocking device capable of stable dynamic sliding cutting was designed, which effectively reduced operation power while ensuring cutting performance. The logarithmicspiral was adopted as the base curve of the disc blade. Through static sliding cutting angle analysis and dynamic-static sliding cutting angle conversion, a segmented blade curve structure was designed, and the optimal dynamic sliding cutting angle for corn stalk cutting was determined to be 37.5°. A “disc-soil-stalk” interaction model was established based on the discrete element method (DEM). Comparative test results showed that the segmented driving disc could ensure cutting efficiency while reducing operation power. On this basis, with cutting power and cutting efficiency as indicators, the key parameters of the disc were optimized through quadratic regression rotation orthogonal tests. The optimal operation parameters of the segmented driving disc were obtained as follows; rotational speed was 310 r/min, forward speed was 2 m/s, and outer radius was 225 mm. A prototype was trial-manufactured according to the optimal operation parameters, and field tests were conducted. The field test results indicated that under the condition of full stalk return, the stalk cutting rate and stubble breaking rate under the optimal operation parameters were 97.85% and 91.53%, respectively. Compared with notched discs and flat discs, the operation power of the segmented driving disc was reduced by 23.69% and 11.69%, respectively, meeting the requirements of corn no-tillage seeding operations in Northeast China. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Efficiency

Controlled terms: Agricultural machinery? - ?Cultivation? - ?Cutting? - ?Discrete element methods? - ?Disks (machine components)? - ?Disks (structural components)? - ?Optimization? - ?Seed? - ?Tillage

Uncontrolled terms: Anti-blocking device? - ?Blockings? - ?Conservation tillage? - ?Cutting efficiency? - ?Discrete elements method? - ?Disk? - ?Dynamic sliding? - ?Operation power? - ?Sliding cutting? - ?Sliding cutting angles

Classification code: 408.1 Structural Members and Shapes? - ?601.2 Machine Components? - ?604.1 Metal Cutting? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?913.1 Production Engineering? - ?1201.5 Computational Mathematics? - ?1201.7 Optimization Techniques

Numerical data indexing: Angular velocity 5.177E+00rad/s, Percentage 1.169E+01%, Percentage 2.369E+01%, Percentage 9.153E+01%, Percentage 9.785E+01%, Size 2.25E-01m, Velocity 2.00E+00m/s

DOI: 10.6041/j.issn.1000-1298.2025.10.024

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

16. Multi-objective Tracking Method for Sheep in Flock Breeding Based on YOLO v9c and Improved ByteTrack

Accession number: 20254519444931

Title of translation: 基于 YOLO v9c 和改进 ByteTrack 的群养羊只多目标跟踪方法

Authors: Zheng, Fang (1, 2); Xia, Chuanyu (1); Du, Xiaoyong (1, 2); Zhou, Yong (3); Tian, Fang (1, 2); Li, Guoliang (1, 2)

Author affiliation: (1) Collage of Informatics, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Smart Breeding Technology, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (3) finchang Animal Husbandry and Veterinary Station, Gansu Province, Jinchang; 737100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 585-595

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Video-based group sheep tracking plays an important role in large-scale, intelligent and unmanned breeding. However, due to the severe occlusion, overlap and excessive movement speed of the flock, it is difficult to accurately track multiple sheep in complex scenes. In order to solve the above problems and improve the adaptability of tracking technology to group sheep, a multi-objective sheep tracking method was proposed based on the combination of YOLO v9c and improved ByteTrack. In terms of object detection, the behavior of sheep was divided into three states: standing, grovel and eating. In terms of multi-target tracking, two improvements were made to ByteTrack; the time and distance matching module (TDMM) was introduced, and the unmatched high-score frame and unmatched trajectory were combined according to the loss time of the lost trajectory and the Euclidean distance to form an identity association coefficient matrix, and the matching was carried out again. The ID delay allocation mechanism was introduced, and in addition to the first frame, the fD allocation module was moved to the third match and conditions were added to prevent premature ID allocation. The results showed that the HOTA was 72.051%, the MOTA was 88.326%, the IDF1 was 88.237%, and the IDSW was 8. Compared with the original ByteTrack, MOTA was increased by 0.242 percentage points, HOTA was increased by 2. 21 percentage points, IDF1 was increased by 5. 734 percentage points, and the number of ID hops was decreased by about 46. 67% . Compared with the representative algorithms Bot — SORT and OC — SORT, the number of ID hops was significantly increased in HOTA and IDF1, and the number of ID hops was greatly reduced. The test results in the complex scenario of multiple sheep showed that the improved ByteTrack algorithm had good multi-target tracking performance, which can effectively improve the accuracy and reliability of group sheep tracking. When the algorithm was combined with the YOLO v9c object detection algorithm to track the sheep in groups and save the tracking results, the average video processing speed was 47. 1 f/s, which was about 37.7% higher than that of the Bot — SORT algorithm of 34.2 f/s. The algorithm can reliably monitor sheep in real time, which can provide an effective technical means for sheep farm managers to detect abnormal sheep behavior and monitor the health status of sheep in time. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Target tracking

Controlled terms: Finite difference method? - ?Network security? - ?Object detection? - ?Object recognition? - ?Object tracking? - ?Signal detection? - ?Trajectories

Uncontrolled terms: Delayed ID allocation? - ?Improved bytetrack? - ?Matchings? - ?Multi objective? - ?Percentage points? - ?Sheep in flock breeding? - ?Targets tracking? - ?Time and distance matching module? - ?Tracking method? - ?YOLO v9c

Classification code: 435.2 Tracking and Positioning? - ?656 Space Flight and Research? - ?716.1 Information Theory and Signal Processing? - ?1106 Computer Software, Data Handling and Applications? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1201.9 Numerical Methods

Numerical data indexing: Percentage 3.77E+01%, Percentage 6.70E+01%, Percentage 7.2051E+01%, Percentage 8.8237E+01%, Percentage 8.8326E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.053

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

17. Dual-branch Semantic Segmentation Network-based Land Parcel Recognition Method with Edge Enhancement

Accession number: 20254519444920

Title of translation: 基于边缘增强的双分支语义分割网络地块识别方法

Authors: Zhang, Hongming (1, 2); Gao, Zhengjie (1); Shen, Yinwei (1); Tang, Hengao (1); Chen, Nuo (1); Yang, Guang (1)

Author affiliation: (1) College of Information Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Shaanxi Engineering Research Center for Intelligent Perception and Analysis of Agricultural Information, Shaanxi, Yangling; 712100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 437-447 and 491

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate and rapid acquisition of land cover data in target areas is crucial for land resource planning and utilization. To address the challenges posed by diverse land cover types and indistinct boundaries in land cover segmentation tasks, an innovative edge-enhanced module within the encoder was introduced. This module was part of a novel edge-enhanced dual-branch semantic segmentation network method, named edge-enhanced dual-branch net (E2DBNet). The proposed E2DBNet integrated finely enhanced edge information into the semantic segmentation model, forming a dual-branch network structure. In the decoder phase, a feature aggregation module was employed to efficiently merge global semantic features with local edge features. Additionally, cross-layer connections from the bottom to the top layers further refined local details by emphasizing critical spatial positions, enhancing the overall accuracy and clarity of the segmentation. The effectiveness of E2DBNet was demonstrated through training and testing on the constructed Yangling segmentation dataset. Comparative experimental results indicated that E2DBNet achieved superior segmentation accuracy across various scenarios, with intersection over union (IoU) and Fl scores of 67. 73% and 93. 25%, respectively. These results represented improvements of 2. 36, 3.7 percentage points, over the compared segmentation models. Furthermore, ablation experiments validated the effectiveness of each individual module within the network. Despite having fewer model parameters, E2DBNet demonstrated a remarkable ability to accurately identify land cover types even with fewer samples, and effectively predicted the main parts of difficult categories. The incorporation of enhanced edge information also ensured clearer boundary distinctions between various land cover types, providing a robust solution for complex land cover segmentation tasks. This advancement held significant potential for improving the precision and efficiency of land cover mapping, ultimately contributing to better land resource management and planning. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Deep learning

Controlled terms: Land use? - ?Latent semantic analysis? - ?Natural resources management? - ?Resource allocation? - ?Semantic Segmentation? - ?Semantic Web? - ?Semantics? - ?Statistical tests

Uncontrolled terms: Deep learning? - ?Edge information? - ?Features fusions? - ?Land cover? - ?Land resources? - ?Land-cover types? - ?Network-based? - ?Resource planning? - ?Segmentation models? - ?Semantic segmentation

Classification code: 403 Urban and Regional Planning and Development? - ?903.2 Information Dissemination? - ?903.3 Information Retrieval and Use? - ?912.2 Management? - ?1101.2.1 Deep Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1106.3.1 Image Processing? - ?1106.7 Computational Linguistics? - ?1106.8 Computer Vision? - ?1202.2 Mathematical Statistics? - ?1501.2.1 Resource Conservation

Numerical data indexing: Percentage 2.50E+01%, Percentage 7.30E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.038

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

18. Fine Classification under Pattern of Forest and Crops Intercropping Based on Multi-source Remote Sensing Data

Accession number: 20254519444927

Title of translation: 基于多源遥感数据的林农间作种植结构精细分类

Authors: Shen, Zhanfeng (1, 2); Kou, Wenqi (1, 2); Wang, Haoyu (1, 2); Zhang, Chi (1, 2); Ma, Yubo (1)

Author affiliation: (1) National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing; 100101, China; (2) College of Resources and Environment, University of Chinese Academy of Sciences, Beijing; 100049, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 429-436

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The pattern of forest and crops intercropping, as a characteristic planting model in Southern Xinjiang, is of great significance for improving agricultural production efficiency and optimizing resource utilization. Focusing on the precise classification of forest-crop intercropping land in Lopu County, Hotan Region, Xinjiang, based on multi-source and high-resolution remote sensing images, the hybrid task cascade (HTC) instance segmentation model was applied to extract the precise boundaries of farmland parcels. Meanwhile, multi-temporal Sentinel — 2 remote sensing data were utilized to calculate spectral characteristic indices of normalized difference vegetation index (NDVI) and normalized difference red-edge 1 (NDrel) during the key reproductive phases of crops. The Transformer temporal model then accurately extracted the information of the forest and crops intercropping planting structure. The distribution of typical forest-food intercropping (walnut and maize) and forest-vegetable intercropping (walnut and radish) patterns in Luopu County was analyzed. The results showed that the overall accuracy (OA) of crop planting structure classification with parcel as the basic unit reached 83.2%. Among them, the forest-food intercropping pattern in Luopu County was dominant, with a classification precision of 78. 4% and a total planting area of 274. 85 km, accounting for 64. 5% of the total area of extracted farmland parcels. The forest-vegetable intercropping pattern was only 15. 55 km, and its classification accuracy was as high as 96. 5%, which was usually manifested as scattered small parcels. The research result can provide a method for the fine identification of forest-crop intercropping planting structures, which was of great significance in guiding the precision management of agriculture in Southern Xinjiang. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 34

Main heading: Remote sensing

Controlled terms: Classification (of information)? - ?Crops? - ?Farms? - ?Precision agriculture? - ?Reforestation? - ?Vegetables? - ?Vegetation

Uncontrolled terms: Fine classification? - ?Forest and crop intercropping? - ?Hybrid task cascade instance segmentation? - ?Hybrid tasks? - ?Multi-source romote sensing data? - ?Multi-Sources? - ?Planting structure? - ?Plantings? - ?Sensing data? - ?Transformer modeling

Classification code: 103 Biology? - ?716.1 Information Theory and Signal Processing? - ?731.1 Control Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.1 Woodlands and Forestry? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?903.1 Information Sources and Analysis? - ?1502.4 Biodiversity Conservation

Numerical data indexing: Percentage 4.00E+00%, Percentage 5.00E+00%, Percentage 8.32E+01%, Size 5.50E+04m, Size 8.50E+04m

DOI: 10.6041/j.issn.1000-1298.2025.10.037

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

19. Design and Experiment of Low-damage Step-type Lifting Device for Potato Harvester

Accession number: 20254419440394

Title of translation: 马铃薯收获机低损阶梯式提升装置设计与试验

Authors: Pan, Zhiguo (1); Mu, Jie (1); Yang, Ranbing (1, 2); Zhang, Huan (1); Wu, Hongzhu (3); Yang, Deqiu (4); Deng, Zhixi (1); Hu, Zhuofan (1); Shu, Yalong (1)

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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 375-385

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A low-damage stepped lifting device for potato combined harvester was designed to address the issue of high tuber damage rates due to the long lifting stroke in hilly mountainous areas. The overall structure and working principle were analyzed, and through the analysis of the potato dropping and lifting process, the key factors affecting tuber damage were identified. A RecurDyn - EDEM coupled simulation model was utilized, and a single-factor experiment was conducted to determine that the angle between the flexible plate and the lifting conveyor belt should be set at 86°. The feed rate at the end of the potato-soil separation device, the operating speed of the separation device, and the operating speed of the low-damage stepped lifting device were considered as experimental factors, with the maximum impact force of the potato serving as the evaluation index. A Box - Behnken central composite design method was employed for the simulation experiments. Variance analysis was performed on the results of these experiments, and response surface analysis was used to study the influence of interactive factors on the test indicators, which led to the determination of the optimal values for the influencing factors. Verification experiments demonstrated that when the angle between the harvester’s flexible plate and the lifting conveyor belt was set at 86°, with a feed rate of 12. 9 t/h, a separation device operating speed of 0. 96 m/s, and a lifting device operating speed of 0. 96 m/s, the average peak value of the electronic potato impact acceleration was found to be 371.04 m/s2, and the maximum average impact force was 61.222 N, which deviated by 1.92 N from the experimental results after parameter optimization. The average tuber damage rate was 1. 11%, which complied with the relevant standards. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Factor analysis

Controlled terms: Damage detection? - ?Flexible structures? - ?Harvesters? - ?Soil testing? - ?Surface analysis

Uncontrolled terms: Conveyor belts? - ?Damage rate? - ?Flexible plates? - ?Hilly and mountainous? - ?Lifting devices? - ?Low damages? - ?Low-loss? - ?Operating speed? - ?Potato harvesters? - ?Separation devices

Classification code: 208 Coatings, Surfaces, Finishes, Films and Deposition? - ?408.1 Structural Members and Shapes? - ?483.1 Soils and Soil Mechanics? - ?821.2 Agricultural Machinery and Equipment? - ?913.3.1 Inspection? - ?1202.2 Mathematical Statistics? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Force 1.92E+00N, Force 6.1222E+01N, Percentage 1.10E+01%, Velocity 3.7104E+02m/s, Velocity 9.60E+01m/s

DOI: 10.6041/j.issn.1000-1298.2025.10.032

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

20. Experimental Analysis of Underwater Noise in Large-scale Intelligent Aquaculture Platforms

Accession number: 20254619496419

Title of translation: 大型智能养殖平台水下噪声特性试验研究

Authors: Chen, Xiahua (1); Chen, Yiwen (1); Qu, Ke (2); Xiong, Yiwen (1); Ling, Wenchang (1); Dong, Yangze (1)

Author affiliation: (1) Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang; 524000, China; (2) College of Oceanography and Meteorology, Guangdong Ocean University, Zhanjiang; 524088, China

Corresponding author: Dong, Yangze(dongyangze@zjblab.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 140-146 and 174

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to alleviate the pressure of nearshore aquaculture on local ecosystems, there has been increasing attentions on offshore aquaculture based on large intelligent fish farm structure. Underwater noise generated by these structures is a critical basis for the assessment of their impact on the environment and the development of intelligent aquaculture. An experimental investigation was conducted on the 60 000 m3 intelligent aquaculture fish cage “HENGYI 1”. Hydrophones were strategically deployed at various depths on the key locations of the cage to record underwater noise, while sound level meters were simultaneously installed to monitor airborne noise. The results indicated that the diesel generator was the predominant noise source, generating significant underwater noise within the 20 ~ 1 500 Hz frequency band and increasing the total sound pressure level by approximately 10 dB. Furthermore, the spectral characteristics of the underwater noise varied with both spatial position and depth across the cage. When feeding operations were halted and the diesel generator was inactive, the underwater noise levels of the platform nearly matched the background noise, with negligible differences across various depths. There was no evidence to support that the noise of the fish cage influenced obviously to the fish, but the behavior changes of the fish may exist. An initial analysis and evaluation to the fish for the underwater noise of the typical rectangular trussed fish cage were performed. The research result can provide a reference for future systematic experimental research and the deployment of intelligent acoustic aquaculture equipment. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Fish

Controlled terms: Acoustic measuring instruments? - ?Acoustic noise? - ?Acoustic noise measurement? - ?Aquaculture? - ?Background noise? - ?Noise generators? - ?Offshore oil well production? - ?Underwater acoustics

Uncontrolled terms: Diesel generators? - ?Fish cages? - ?Large-scale aquaculture platform? - ?Large-scales? - ?Measurement methods? - ?Noise measurement method? - ?Noise measurements? - ?Off shore aquaculture? - ?Off-shore? - ?Underwater noise

Classification code: 511.1 Oil Field Production Operations? - ?716.1 Information Theory and Signal Processing? - ?751.1 Acoustic Waves? - ?751.4 Acoustic Noise? - ?821.4 Agricultural Methods? - ?822.3 Food Products? - ?942.1.2 Acoustical Instruments

Numerical data indexing: Decibel 1.00E+01dB, Frequency 5.00E+02Hz, Size 0.00E00m

DOI: 10.6041/j.issn.1000-1298.2025.10.014

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

21. Instances Segmentation and Extraction of Phenotypic Parameters of Gentiana scabra Bunge Roots Based on FDC Mamba

Accession number: 20254619496475

Title of translation: 基于 FDC Mamba 的关龙胆根茎实例分割与表型参数提取

Authors: Cui, Hongguang (1); Liu, Haitao (1); Ma, Youze (1); Huang, Wenzhong (2); Li, Hongbo (3); Wang, Tiejun (1)

Author affiliation: (1) College of Engineering, Shenyang Agricultural University, Shenyang; 110866, China; (2) Fushun Agriculture and Rural Affairs Development Service Center, Fushun; 113006, China; (3) College of Horticulture, Shenyang Agricultural University, Shenyang; 110866, China

Corresponding author: Wang, Tiejun(tiejunwang@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: 10

Issue date: October 2025

Publication year: 2025

Pages: 500-511

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the problems of challenges of difficult feature extraction and insufficient recognition accuracy due to the highly similar phenotypic characteristics of stem scars and residual stem bases in Gentiana scabra Bunge roots, as well as their small size and complex morphology, the Focal Modulation DyHead seg Context Guided Mamba (FDC Mamba) Gentiana scabra Bunge roots instances segmentation was proposed. Firstly, to solve the problems of blurred boundaries between adjacent root fibers and overlapping in entangled parts of Gentiana scabra Bunge, the object detection Mamba (ODMamba) backbone network was introduced to supplement texture details and enhance structural consistency. Secondly, by integrating the Focal Modulation and Context Guided structural, the multi-scale perception abilities and detail segmentation abilities were enhanced. Finally, the dynamic head structure was combined with the Auxiliary Head training strategies to develop a training structure for instance segmentation, DyHead-seg, which improved information transmission efficiency and optimized the learning process. The proposed model was compared with other commonly used instance segmentation models (YOLO series, Mask R CNN, PointRend, HTC, SOLOv2, RT DETR, HYPER), different feature pyramid architecture modules (RepBN, AIFI, LSKA), and different downsampling structure modules (SRFD, ADown, CARAFE, EUCB, Gold YOLO, HWD, PSConv, SODConv, WaveletPool) on the Gentiana scabra Bunge roots datasets. The improved model successfully completed the instance segmentation of Gentiana scabra Bunge roots. FDC Mamba model had high accuracy in the positioning of root and stem margins and small areas. The Box type and Mask type P, AP50, and AP95 were increased by 6. 52, 5. 09, 5. 44 percentage points and 4. 49, 2. 68, 1. 16 percentage points, respectively. Based on the segmentation results, four phenotypic parameters extraction methods for Gentiana scabra Bunge sorting were figured put, including root length, root thickness degrees, impurity rate, and chromaticity. The experimental results showed that the proposed model achieved a Mask type P of 87. 12%, which was 4. 49 percentage points higher than that of the baseline model. The relative errors between the extraction results of Gentiana scabra Bunge phenotypic parameters and the manual measurement results were all within 5% . The research result had high accuracy in extracting phenotypic features of roots Chinese medicinal materials represented by Gentiana scabra Bunge, providing a foundation for subsequent processing technology and equipment research and development. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Object detection

Controlled terms: Extraction? - ?Feature extraction? - ?Morphology? - ?Network architecture? - ?Object recognition? - ?Parameter extraction? - ?Sorting? - ?Textures

Uncontrolled terms: Deep learning? - ?FDC mamba? - ?Features extraction? - ?Gentiana scabra bunge? - ?High-accuracy? - ?Instance segmentation? - ?Parameters extraction? - ?Percentage points? - ?Phenotype parameter extraction? - ?Recognition accuracy

Classification code: 214 Materials Science? - ?802.3 Chemical Operations? - ?1101.2 Machine Learning? - ?1105.2 Internet and Web Technologies? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing? - ?1106.5 Computer Applications? - ?1106.8 Computer Vision? - ?1202 Statistical Methods

Numerical data indexing: Percentage 1.20E+01%, Percentage 5.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.044

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

22. Effects of Different Mulch Irrigation Methods on Soil Water-salt-nitrogen Dynamics in Spring Maize Fields

Accession number: 20254619496437

Title of translation: 不同覆膜灌溉方式下春玉米田土壤水盐氮动态变化规律

Authors: Dong, Qin’ge (1, 2); Liu, Qingyue (3); Niu, Yuting (1, 2); Zhou, Yuming (1, 2); Zhou, Junwei (1, 2); Feng, Hao (1, 2)

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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 671-683

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to elucidate the regulatory effects of different combinations of mulching and irrigation methods on mitigating the multi-factor coupled stresses in spring maize fields of the Hetao Irrigation District，including water scarcity，surface salt accumulation，and low early spring temperatures hindering maize growth，a field experiment was conducted with four treatments： border irrigation without mulching （ CK），transparent film mulching with border irrigation （ QB），transparent film mulching with furrow irrigation on ridges （ GB），and black film mulching with furrow irrigation on ridges （ GH），to investigate their impacts on soil water-salt-nitrogen transport，spring maize growth，yield components，and water-nitrogen use efficiency． The results demonstrated that soil water content and nitrate nitrogen content were decreased in the order of GB，GH，QB，CK； significant salt accumulation was occurred in the surface soil under CK，whereas GB effectively reduced surface soil salinity and nitrogen leaching on the ridges，inhibited upward salt movement，increased soil nitrate nitrogen content，and enhanced soil water availability． Plant height and dry matter accumulation ranked from the highest to the lowest was as follows： GB，GH，QB and CK； the leaf area index （ LAI） was significantly higher under GB，GH，and QB compared with that under CK，but it showed no significant differences among these three mulched treatments，indicating LAI was primarily determined by mulching presence． Regarding yield，GB resulted in a significant increase of 13. 64% over QB and 32. 04% over CK，and although higher than GH，the difference was not statistically significant （ P＞0. 05）． Water use efficiency （ WUE） and nitrogen use efficiency （ NUE） were the highest under GH，followed by GB，QB，and then CK，but differences between GB and GH were not significant． In conclusion，the GB treatment demonstrated the most favorable overall performance； therefore，adopting the ridge-furrow system with transparent plastic film mulching in the Hetao Irrigation District of Inner Mongolia was recommended to enhance spring maize yield and resource use efficiency. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 40

Main heading: Nitrates

Controlled terms: Agribusiness? - ?Efficiency? - ?Film growth? - ?Grain (agricultural product)? - ?Irrigation? - ?Leaf springs? - ?Nitrogen? - ?Plants (botany)? - ?Soils? - ?Water resources

Uncontrolled terms: Fertilizer salts? - ?Film mulching? - ?Furrow irrigation? - ?Hetao irrigation districts? - ?Maize yield? - ?Ridge film furrow irrigation? - ?Salt transport? - ?Spring maize? - ?Spring maize yield? - ?Water-fertilizer-salt transport

Classification code: 103 Biology? - ?214 Materials Science? - ?444 Water Resources? - ?483.1 Soils and Soil Mechanics? - ?601.2 Machine Components? - ?804 Chemical Products? - ?804.2 Inorganic Compounds? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?913.1 Production Engineering

Numerical data indexing: Percentage 4.00E+00%, Percentage 6.40E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.061

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

23. Method of Feeding Timing in Crab Ponds Based on Water Quality Parameter Fusion-Multivariate DeepAR Algorithm

Accession number: 20254619496436

Title of translation: 基于水质参数融合-多元 DeepAR 算法的蟹塘投饲时机判断方法

Authors: Han, Yusheng (1); Wang, Xinmeng (1); Wang, Hao (1); Hu, Jianing (2); Ren, Zhenhui (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding; 071001, China; (2) Shijiazhuang Xinnong Machinery Co.,Ltd., Shijiazhuang; 052400, China

Corresponding author: Ren, Zhenhui(renzh@hebau.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 156-164 and 183

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In China’s crab pond aquaculture industry, feed is mostly fed at fixed times, with little consideration of the actual feeding intensity of river crabs, resulting in low feed utilization efficiency. Changes in water quality in crab ponds directly affected the feeding intensity of river crabs. Based on this, combined with the multivariate DeepAR algorithm, a method for determining the optimal feeding time in crab ponds was designed. Eight water quality parameters, including dissolved oxygen content, water temperature, pH value, oxidation-reduction potential, electrical conductivity, total dissolved solids, ammonia nitrogen content, and turbidity, were selected as evaluation indicators. The three-scale analytic hierarchy process was chosen as the subjective weighting method, the CRITIC method based on the entropy weight method as the objective weighting method, and game theory as the combined weighting method. A crab pond water quality scoring method was constructed by using the subjective-objective combined weighting method. Taking time series, dissolved oxygen content, and turbidity as characteristic values, and the crab pond water quality score as the target, the multivariate DeepAR algorithm was used to predict the changes in the water quality score curve over the next 12 h. The peak of the curve represented the maximum water quality score, at which time the feeding intensity of river crabs was the highest. Therefore, the peak stage of the curve was defined as the optimal feeding time. The results showed that the crab pond water quality scoring method had a good correlation with the actual feeding activities of river crabs, and the multivariate DeepAR algorithm can better predict the changes in the water quality score curve over the next 12 h, which can guide the feeding process of river crabs. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Game theory

Controlled terms: Ammonia? - ?Analytic hierarchy process? - ?Aquaculture? - ?Biochemical oxygen demand? - ?Dissolution? - ?Dissolved oxygen? - ?Feeding? - ?Lakes? - ?Quality control? - ?Rivers ? - ?Scales (weighing instruments)? - ?Shellfish? - ?Water quality

Uncontrolled terms: Dissolved oxygen contents? - ?Feeding intensity of river crab? - ?Feeding time? - ?Feeding timing in crab pond? - ?Fusion of water quality parameter? - ?Multivariate deepar algorithm? - ?Optimal feeding? - ?Pond water? - ?Water quality parameters? - ?Weighting methods

Classification code: 103 Biology? - ?407 Maritime and Port Structures; Rivers and Other Waterways? - ?444.1 Surface Water? - ?445 Water Treatment? - ?445.2 Water Analysis? - ?691.2 Materials Handling Methods? - ?802.2 Chemical Reactions? - ?802.3 Chemical Operations? - ?804.2 Inorganic Compounds? - ?821.4 Agricultural Methods? - ?912.2 Management? - ?913.3 Quality Assurance and Control? - ?942.1.7 Special Purpose Instruments? - ?1201.4 Applied Mathematics

Numerical data indexing: Time 4.32E+04s

DOI: 10.6041/j.issn.1000-1298.2025.10.016

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

24. Construction and Operation Test of Simple Recirculating Aquaculture System for Polyodon spathula

Accession number: 20254619496463

Title of translation: 简易式匙吻鲟循环水养殖系统构建与运行试验

Authors: Lei, Xiang (1); Fan, Hao (1); Wang, Deqing (1); Zhu, Ming (1, 2); Gao, Jian (2, 3); Wan, Peng (1, 2); Li, Zhibing (4)

Author affiliation: (1) College of Engineering, Huazhong Agricultural University, Wuhan; 430070, China; (2) Key Laboratory of Aquaculture Facilities Engineering, Ministry of Agriculture and Rural Affairs, Wuhan; 430070, China; (3) College of Fisheries, Huazhong Agricultural University, Wuhan; 430070, China; (4) Tianmen Yuhui Aquaculture Farm, Tianmen; 431719, China

Corresponding author: Wan, Peng(wanpeng09@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: 10

Issue date: October 2025

Publication year: 2025

Pages: 119-129

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of complex composition, high construction and operation cost of industrial recirculating aquaculture system, and certain difficulty in popularization and application in aquaculture, a simple recirculating aquaculture system was constructed. The recirculating aquaculture system was composed of a reservoir, a breeding pond, a water level control device, a micro-filter, a biological filter and a circulating pump. Based on the principle of gravity self-flow, the equipment of each breeding facility was configured, and the diameter ratio of the water circulation pipeline of the breeding system was optimized. A 90-day breeding experiment was carried out with the Polyodon spathula as the object to evaluate the stage operation effect and economy of the system. The experimental results showed that the water circulation of the system was stable; during the breeding process, the water temperature, dissolved oxygen concentration and pH value of the system ranged from 12. 30℃ to 24. 57℃, 3. 96 mg/ L to 9. 32 mg/ L and 6. 82 to 7. 60, respectively. The average values of ammonia nitrogen concentration, nitrite nitrogen concentration and nitrate nitrogen concentration were (1. 26 ± 0. 62) mg/ L, (0. 38 ± 0. 12) mg/ L and (72. 39 ± 14. 94) mg/ L, respectively. The removal rates of ammonia nitrogen and nitrite nitrogen were 49. 07% and 62. 00%, respectively. The average weight of Polyodon spathula was increased from (144. 83 ±15. 24) g to (437. 93 ±60. 98) g. The final culture density was 15. 48 kg/ m3, the feed coefficient was 1. 08, and the survival rate was 96. 16% . Based on the breeding cycle of 12 months, the feeding cost was estimated to be about 17. 57 RMB / kg. The simple recirculating aquaculture system constructed ran stably, the treatment capacity of aquaculture water was good, and the cultivation of Polyodon spathula can obtain good economic benefits, which can provide reference for the popularization and application of industrial recirculating aquaculture system. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 35

Main heading: Ammonia

Controlled terms: Costs? - ?Filters (for fluids)? - ?Lakes? - ?Leveling (machinery)? - ?Nitrates? - ?Nitration? - ?Nitrogen removal? - ?pH? - ?Reservoirs (water)? - ?Stages ? - ?Water filtration? - ?Water treatment

Uncontrolled terms: Ammonia-nitrogen? - ?Construction tests? - ?Nitrite nitrogen? - ?Nitrogen concentrations? - ?Operation tests? - ?Polyodon spathulum? - ?Recirculating aquaculture system? - ?Simple style? - ?Simple++? - ?Water circulation

Classification code: 301.1.1 Liquid Dynamics? - ?402.2 Public Buildings? - ?407 Maritime and Port Structures; Rivers and Other Waterways? - ?407.1.1 Hydrotechnical Engineering Structures? - ?441.2 Reservoirs? - ?444.1 Surface Water? - ?445.1 Water Treatment Techniques? - ?801 Chemistry? - ?802.2 Chemical Reactions? - ?802.3 Chemical Operations? - ?804.2 Inorganic Compounds? - ?821.4 Agricultural Methods? - ?911 Cost and Value Engineering; Industrial Economics? - ?1502.1.1.4 Pollution Control

Numerical data indexing: Age 9.996E-01yr, Linear density 4.80E+01kg/m, Mass density 3.20E-02kg/m3, Mass density 9.60E-02kg/m3 to 9.00E-03kg/m3, Percentage 0.00E00%, Percentage 1.60E+01%, Percentage 7.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.012

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

25. Citrus Pose Estimation Based on YOLO v5－MNv4S and RANSAC－GN

Accession number: 20254619496451

Title of translation: 基于 YOLO v5－MNv4S 与 RANSAC－GN 的柑橘姿态估计方法

Authors: Li, Li (1, 2); Zhang, Guanming (1, 2); Zhang, Yunfeng (1, 2); Liang, Jiyuan (1, 2); Chun, Changpin (3)

Author affiliation: (1) College of Engineering and Technology, Southwest University, Chongqing; 400715, China; (2) Chongqing Key Laboratory of Agricultural Equipment for Hilly and Mountainous Regions, Chongqing; 400715, China; (3) Citrus Research Institute, Southwest University, Chongqing; 400700, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 558-566

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenge of estimating the variable pose of citrus in orchard environments and enhance the citrus harvesting success rate，a lightweight target detection network，YOLO v5－MNv4S，along with an improved random sample consensus－Gauss Newton （ RANSAC－GN）point cloud processing algorithm was introduced． The citrus pose was defined based on actual citrus growth and the final harvesting process，constructing a real-time citrus pose estimation system． The YOLO v5s backbone network was optimized to be a lightweight feature extraction network （ MNv4－Conv－S），which significantly reduced the training parameters so that the final output network weights were lightweight，reducing the amount of computation and improving the recognition efficiency． Additionally，the CA attention mechanism was incorporated，and the loss function was replaced with SIoU，addressing the weak feature extraction ability of the lightweight network． These improvements resulted in a lightweight YOLO v5 － MNv4S network with superior detection capabilities compared with YOLO v5s．After the D435i image acquisition，YOLO v5 －MNv4S was input to detect the target bounding box by using the pinhole model to output the citrus regional point cloud，segment the citrus surface point cloud，combined with the improved RANSAC － GN point cloud algorithm to fit accurate and stable citrus parameters，then fusion of the stem-end spatial coordinates，and ultimately the output of citrus spatial pose results to be harvested． Ablation experiments and network comparisons demonstrated that the lightweight YOLO v5 － MNv4S achieved 93. 1% accuracy，with only 14. 7% of the parameters found in YOLO v5s． Compared with YOLO－Ghost，YOLO v7，YOLO v8，and other networks，it offered the best recognition accuracy with significantly reduced parameters． Experimental results for citrus localization and pose recognition showed that the citrus parameter fitting error using RANSAC － GN was （ 0. 18，0. 19，0. 44） mm，and the pose estimation error was 2. 56°． The pose estimation was accurate，and the estimation results of citrus pose in real orchard environments were consistent with real citrus． The research result can recognize citrus pose in orchard environments and provide technical support for structured citrus orchard mechanical harvesting equipment. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Object detection

Controlled terms: Ablation? - ?Extraction? - ?Feature extraction? - ?Gesture recognition? - ?Harvesting? - ?Image enhancement? - ?Object recognition? - ?Orchards? - ?Parameter estimation

Uncontrolled terms: Citrus harvesting? - ?Citrus picking? - ?Cloud processing? - ?Features extraction? - ?Lightweight network? - ?Mobilenetv4? - ?Objects detection? - ?Point cloud processing? - ?Point-clouds? - ?Pose-estimation

Classification code: 214 Materials Science? - ?302.2 Heat Transfer? - ?802.3 Chemical Operations? - ?821.4 Agricultural Methods? - ?1101.2 Machine Learning? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1201 Mathematics? - ?1202 Statistical Methods

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

DOI: 10.6041/j.issn.1000-1298.2025.10.050

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

26. Detection of Physiological Response to Heavy Metal Stress in Rice (Oryza sativa L.) Leaves Based on Electrical Impedance Spectroscopy

Accession number: 20254619496423

Title of translation: 基于电阻抗谱的水稻叶片重金属胁迫生理响应检测

Authors: Li, Meiqing (1, 2); Li, Jinyang (1, 2); Xing, Deke (1, 2)

Author affiliation: (1) School of Agricultural Engineering, Jiangsu University, Zhenjiang; 212013, China; (2) Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, 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: 10

Issue date: October 2025

Publication year: 2025

Pages: 512-519

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The aim was to use a non-destructive rapid monitoring method to timely reflect the physiological reaction and detection the response of rice leaves caused by heavy metals. A potted rice experiment was used, which treated with three kinds of exogenous heavy metals ions (copper, zinc, nickel) in soil, and four levels of heavy metal content were set. The electrical impedance spectroscopy technique can be employed for quickly diagnosis of the nutritional status of plants and serves as a nondestructive diagnostic method in agriculture. Therefore, rice leaves treated for 21 days were subjected to EIS analysis, followed by measurement of physiological response indexes to establish the correlation between physiological indexes and EIS parameters. Experimental results demonstrated a strong correlation changes in the electrical impedance Re / Ri of rice seedling leaves and electrolyte leakage rate, MDA levels, as well as leaf selection. Furthermore, under copper (Cu) treatments, the Re / Ri values of the first unfolding leaf at the top exhibited a higher correlation with MDA levels and electrolyte leakage rate, reaching coefficients of 0. 956 and 0. 938. Similarly, after zinc (Zn) treatment, the Re / Ri values of the second unfolded leaf showed significant correlations with MDA levels and electrolyte leakage rate, achieving coefficients of 0. 944 and 0. 969 respectively. Additionally, following nickel (Ni) treatment, both the first and second unfolded leaves displayed high correlations between their impedance values and MDA levels, proline content as well as leakage rate. In summary, these findings indicated that measuring Re / Ri in rice leaves can serve as an effective non-destructive method for rapidly assessing heavy metal stress on rice plants by reflecting various physiological indicators. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Correlation methods

Controlled terms: Agriculture? - ?Copper? - ?Diagnosis? - ?Electric impedance? - ?Electric impedance measurement? - ?Heavy metals? - ?Metal ions? - ?Nondestructive examination? - ?Physiological models? - ?Physiology ? - ?Plants (botany)

Uncontrolled terms: Correlation? - ?Detection? - ?Electrical impedance spectroscopy? - ?Electrolyte leakage? - ?Heavy metal stress? - ?Leakage rates? - ?Physiological response? - ?Rice (Oryza sativa L.)? - ?Rice leaves? - ?Stresses response

Classification code: 101.1 Biomedical Engineering? - ?102.1 Medicine? - ?103 Biology? - ?201.1 Metallurgy and Metallography? - ?201.1.1 Metallurgy? - ?202.4.1 Copper? - ?215.2.1 Non-mechanical Properties Testing Equipment and Methods? - ?701.1 Electricity: Basic Concepts and Phenomena? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?941.3 Electric Variables Measurements? - ?1202.2 Mathematical Statistics

Numerical data indexing: Age 5.754E-02yr

DOI: 10.6041/j.issn.1000-1298.2025.10.045

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

27. Natural Language Understanding in Agriculture: a Comprehensive Review of Technologies and Applications

Accession number: 20254619496441

Title of translation: 农业领域自然语言理解技术应用综述

Authors: Li, Xiaopeng (1); Xiang, Yuyun (1); Zhang, Peijun (1); Gao, Yunfan (1); Zhou, Shanlin (1); Rong, Yanpeng (1); Li, Shuqin (1)

Author affiliation: (1) College of Information Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China

Corresponding author: Li, Shuqin(lsq_cie@nwsuaf.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 200-222

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Natural language understanding (NLU), a pivotal branch of artificial intelligence, has demonstrated considerable potential in the agricultural domain thanks to its strengths in text processing, knowledge extraction and intelligent decision support. The evolution and core methodologies of NLU was reviewed and representative studies were surveyed across multiple agricultural scenarios, including agricultural text information extraction, construction of agricultural knowledge graphs, intelligent interaction with agricultural equipment and services, and the mining of scientific literature and patents. These applications have significantly enhanced the intelligence level of agricultural information acquisition and processing, providing effective support for agricultural production and management. Despite promising progress, NLU applications in agriculture still face several challenges: linguistic diversity and dialectal variation, small-sample learning and data-annotation scarcity, cross-modal data fusion and semantic alignment, efficient model deployment, and data-privacy protection in pursuit of sustainable development. Looking ahead, the rapid advances in self-supervised learning, transfer learning and multimodal intelligent agriculture were expected to empower NLU to play an even greater role in precision farming, real-time decision support and the broader quest for agricultural sustainability. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 199

Main heading: Decision support systems

Controlled terms: Agricultural implements? - ?Agricultural machinery? - ?Construction equipment? - ?Data mining? - ?Data privacy? - ?Extraction? - ?Information retrieval? - ?Learning algorithms? - ?Machine learning? - ?Natural language processing systems ? - ?Semantics? - ?Smart agriculture? - ?Sustainable agriculture? - ?Text processing

Uncontrolled terms: Agricultural knowledge graph? - ?Intelligent question-answering? - ?Knowledge graphs? - ?Natural language understanding? - ?Question Answering? - ?Review of technologies? - ?Smart agricultures? - ?Technologies and applications? - ?Text information extraction? - ?Text-processing

Classification code: 405.1 Construction Equipment? - ?802.3 Chemical Operations? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?903.1 Information Sources and Analysis? - ?903.2 Information Dissemination? - ?903.3 Information Retrieval and Use? - ?912.2 Management? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1106.2 Data Handling and Data Processing? - ?1106.2.1 Data Mining? - ?1106.7 Computational Linguistics? - ?1108 Security and Privacy? - ?1501.1 Sustainable Development

DOI: 10.6041/j.issn.1000-1298.2025.10.020

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

28. Dynamic Modeling and Performance Analysis of End-articulated Three-translational Parallel Mechanism with Multiple Driving Modes

Accession number: 20254619506391

Title of translation: 具有多驱动模式的末端铰接三平动并联机构动力学建模与性能分析

Authors: Liang, Dong (1, 2); Shi, Haohao (3); Chang, Boyan (1, 2); Cui, Manjun (1); Zhang, Junpeng (1)

Author affiliation: (1) School of Mechanical Engineering, Tiangong University, Tianjin; 300387, China; (2) Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tiangong University, Tianjin; 300387, China; (3) School of Aeronautics and Astronautics, Tiangong University, Tianjin; 300387, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 758-769 and 801

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Oriented by the complex working environment and multi-task requirements of robots in the high-end manufacturing field，a type of end-hinged parallel mechanism with multiple driving modes was studied for its design，kinematic/dynamic modeling，and performance analysis． Based on the existed mechanism，by constructing various auxiliary structures，a 3-DOF translational parallel mechanism that realizing eight driving modes was proposed，and the conceptual design was given． On the premise of positional analysis for the total joints，the Jacobian matrix and the velocity/acceleration mapping models under multiple driving modes were obtained，and a complete system dynamics model under multiple driving modes was established by using the principle of virtual power． By virtue of virtual prototyping technology，a Simscape multi-body physical simulation model was developed to implement inverse dynamics simulation under multiple driving modes． The comparison result between the simulation and numerical calculation indicated that the maximum torque error did not exceed 0. 03 N·m，verifying the correctness of the dynamics model under multiple driving modes． Finally，based on the task-space dynamic model of system，the dynamic dexterity index was defined to evaluate the local dynamic performance of the robot under different driving modes to further reveal the effectiveness and advantage of multiple driving modes． The research results can lay a theoretical foundation for the design，modeling，optimization，and practical applications the robot in future. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Dynamic models

Controlled terms: Conceptual design? - ?Dynamics? - ?Inverse problems? - ?Jacobian matrices? - ?Machine design? - ?Optimization? - ?Robots? - ?Virtual prototyping? - ?Virtual reality

Uncontrolled terms: Driving mode? - ?Dynamic model analysis? - ?Dynamic performance analysis? - ?Dynamics models? - ?Multi-body simulation experiment? - ?Multibody simulations? - ?Multiple drive mode parallel mechanism? - ?Multiple drive modes? - ?Parallel mechanisms? - ?Performances analysis

Classification code: 601 Mechanical Design? - ?731.5 Robotics? - ?904 Design? - ?1106.5 Computer Applications? - ?1107.1 Virtual Reality Technology? - ?1201 Mathematics? - ?1201.2 Calculus and Analysis? - ?1201.7 Optimization Techniques? - ?1301.1.1 Mechanics? - ?1301.7 Statistical and Nonlinear Physics

Numerical data indexing: Torque 3.00E+00N.m

DOI: 10.6041/j.issn.1000-1298.2025.10.069

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

29. Path Planning Method of Multi-joint Manipulator Based on Improved RRT Algorithm

Accession number: 20254619506384

Title of translation: 基于改进 RRT 算法的多关节机械臂路径规划方法研究

Authors: Liang, Xifeng (1); Chen, Kun (1); Yao, Baoguo (1)

Author affiliation: (1) School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou; 310018, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 792-801

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of high randomness in sampling，large search space，low efficiency，and non-smooth paths in the rapidly-exploring random tree （ＲＲT） algorithm for 3D path planning of robotic arms，an improved ＲＲT algorithm based on spatial guidance points combined with a goal-biased sampling strategy was proposed． Before generating the ＲＲT tree，spatial guidance points were introduced，and a goal-biased strategy was employed during sampling to reduce the randomness of the algorithm’s search． When generating new nodes，adaptive step-size adjustment was used to accelerate the search in open areas and reduce the likelihood of missing better points near obstacles． After the initial path was generated，a greedy strategy was applied to remove redundant nodes from the path． Finally，Hermite interpolation was used to smooth the path，improving path quality． Simulation and experimental results demonstrated that the improved algorithm reduced the average search time by 70． 83% and the average path length by 22． 71% compared with the ＲＲT algorithm． Using a 6-DOF manipulator for obstacle avoidance experiments showed that the improved ＲＲT algorithm reduced the average search time by 79． 95% and the average path length by 30． 93%，with a path planning success rate of 96． 77%． The improved algorithm can quickly plan collision-free paths in 3D space，laying a theoretical foundation for the motion control of multi-joint robotic arms. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Motion planning

Controlled terms: Collision avoidance? - ?Decision trees? - ?Forestry? - ?Motion control? - ?Random forests? - ?Random processes? - ?Redundant manipulators? - ?Robotic arms

Uncontrolled terms: Adaptive step size? - ?Average path length? - ?Average Search Time? - ?Bias sampling? - ?Improved * algorithm? - ?Joint manipulators? - ?Multi-joint? - ?Path planning method? - ?Spatial guide point? - ?ＲＲT algorithm

Classification code: 101.6.1 Robotic Assistants? - ?691.1 Materials Handling Equipment? - ?731.3 Specific Variables Control? - ?731.5 Robotics? - ?821.1 Woodlands and Forestry? - ?914.1 Accidents and Accident Prevention? - ?961 Systems Science? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1201.5 Computational Mathematics? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1202.1 Probability Theory

Numerical data indexing: Percentage 7.10E+01%, Percentage 7.70E+01%, Percentage 8.30E+01%, Percentage 9.30E+01%, Percentage 9.50E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.072

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

30. Incomplete Dissolved Oxygen Data Interpolation Based on Lagged Causality Analysis and Improved SSIM

Accession number: 20254619496429

Title of translation: 基于滞后因果分析和改进 SSIM 的溶解氧含量数据插补算法

Authors: Liu, Shijing (1, 2); Zhang, Jiapeng (1); Qian, Cheng (1); Tu, Xueying (1); Nie, Pengcheng (3)

Author affiliation: (1) Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai; 200092, China; (2) Sanya Oceanographic Insitution, Ocean University of China, Sanya; 572011, China; (3) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou; 310058, China

Corresponding author: Qian, Cheng(qiancheng@fmiri.ac.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 175-183

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Based on the industrial development demands of intelligent aquaculture production operations in aquaculture facilities, an incomplete dissolved oxygen data imputation algorithm was proposed based on lag causality analysis and an improved sequence-to-sequence imputation model (SSIM) to address issues related to systematic or accidental loss of aquaculture environmental data. Firstly, environmental data such as water quality and meteorological data were collected at fixed sampling frequencies. Using the granger causality (GC) theory, the component-wise long short-term memory (cLSTM) method was employed to analyze the lag correlation between different environmental variables and dissolved oxygen time series data. The environmental variables with significant causal relationships were selected to construct the training sample set. Secondly, the SSIM framework was used to implement the imputation of missing dissolved oxygen data, and an optimization method of the SSIM model was proposed by combining a two-layer bidirectional long short-term memory (BiLSTM) structure and Dropout regularization to improve the model’s ability to represent complex features. Experimental results showed that the proposed method effectively improved the data imputation accuracy. The mean absolute error (MAE) and root mean square error (RMSE) for 1-hour missing data reached 0. 04 and 0. 05, respectively; for 3-hour missing data, the errors were 0. 16 and 0. 17, and for 5-hour missing data, the errors were 0. 43 and 0. 45. The research result can provide effective technical support for data quality control in aquaculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Dissolved oxygen

Controlled terms: Aquaculture? - ?Data accuracy? - ?Data quality? - ?Dissolution? - ?Errors? - ?Interpolation? - ?Mean square error? - ?Quality control? - ?Statistical tests

Uncontrolled terms: Bidirectional long short-term memory? - ?Data interpolation? - ?Data interpolation algorithm? - ?Dissolved oxygen content prediction? - ?Dissolved oxygen contents? - ?Interpolation algorithms? - ?NGC? - ?Sequence-to-sequence imputation model? - ?Short term memory? - ?Smart aquaculture

Classification code: 445 Water Treatment? - ?731.1.1 Error Handling? - ?802.2 Chemical Reactions? - ?802.3 Chemical Operations? - ?821.4 Agricultural Methods? - ?913.3 Quality Assurance and Control? - ?1106.2 Data Handling and Data Processing? - ?1201.9 Numerical Methods? - ?1202.2 Mathematical Statistics

Numerical data indexing: Time 1.08E+04s, Time 1.80E+04s, Time 3.60E+03s

DOI: 10.6041/j.issn.1000-1298.2025.10.018

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

31. Design and Experiment of Chain-feeding Pineapple Harvester

Accession number: 20254619494077

Title of translation: 链喂入式菠萝采收机设计与试验

Authors: Liu, Tianhu (1); Zhang, Jinchong (1); Lian, Zhuoqian (1); Li, Jiahao (1); Liang, Zhaozheng (1); Liu, Shuyang (2); Zhou, Xuhui (1)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) College of Agronomy, South China Agricultural University, 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: 10

Issue date: October 2025

Publication year: 2025

Pages: 386-396

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the low harvest completeness rate in mechanized pineapple harvesting caused by significant plant height variations and lodging, a chain-feeding pineapple harvester was designed. The working mechanism of the harvester was elaborated, and its structural parameters, such as the installation height of the harvesting unit and the spacing of the feeding chains, were determined based on the physical characteristics of pineapple plants. By establishing a kinematic model of pineapple plants and a contact mechanics model between pineapple fruits and the integrated picking device, the critical parameters were derived as follows: the minimum lifting inclination angle of the seedling divider was 20°, the maximum allowable rotational speed of the fruit-detach device was 62 r/min, and the minimum rotational speed of the feeding chain was 25 r/min. Further analysis identified that seedling divider inclination angle, speed ratio between the mount ring of fruit push finger and the chain gear and the rotate speed of feeding chain were three key factors affecting harvesting performance. A three-factor, three-level bench test was conducted by using the Box Behnken experimental design principle, by taking harvest completeness rate and fruit damage rate as evaluation metrics. Regression models linking test indicators and factors were developed via Design-Expert 13. 0 software, and the optimal parameter combination was derived through model analysis. Test results demonstrated that with a seedling divider inclination angle of 28°, a speed ratio of 0. 75 between the mount ring of fruit push finger and the chain gear, and a rotate speed of 44 r/min with feeding chain, the harvest completeness rate reached 92. 22% and the fruit damage rate was 7. 1%. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Feeding

Controlled terms: Chains? - ?Elasticity? - ?Fruits? - ?Harvesters? - ?Harvesting? - ?Plants (botany)? - ?Regression analysis? - ?Seed? - ?Software testing

Uncontrolled terms: Chain-type? - ?Chain-type feeding? - ?Damage rate? - ?Harvest completeness rate? - ?Inclination angles? - ?Pineapple harvester? - ?Plant height? - ?Rotate speed? - ?Rotational speed? - ?Speed ratio

Classification code: 103 Biology? - ?214.1.3 Elasticity, Plasticity, Creep and Deformation? - ?601.2 Machine Components? - ?602.1 Mechanical Drives? - ?691.2 Materials Handling Methods? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?1106.9 Computer Software? - ?1202.2 Mathematical Statistics

Numerical data indexing: Angular velocity 1.0354E+00rad/s, Angular velocity 4.175E-01rad/s, Angular velocity 7.348E-01rad/s, Percentage 1.00E00%, Percentage 2.20E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.033

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

32. Design and Testing of Unmanned Boat System for In-situ Counting of Sea Cucumbers in Pond Aquaculture

Accession number: 20254619496415

Title of translation: 面向池塘养殖的水下海参原位计数无人船系统设计与试验

Authors: Liu, Xiaoyang (1, 2); Wang, Wenliang (1, 2); Zheng, Xinyu (1, 2); Chen, Jiaying (1, 2); Chen, Qijun (3); Zhao, Yonggang (3); Ling, Yuanshan (1, 4)

Author affiliation: (1) School of Information Engineering, Dalian Ocean University, Dalian; 116023, China; (2) Dalian Key Laboratory of Smart Fisheries, Dalian; 116023, China; (3) Dalian Xinyulong Marine Biological Seed Industry Science and Technology Co., Ltd., Dalian; 116007, China; (4) Key Laboratory of Facilities Fisheries, Ministry of Education, Dalian Ocean University, Dalian; 116023, China

Corresponding author: Ling, Yuanshan(linyuanshan@dlou.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 82-93

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of high risk, high cost, and low efficiency in existing underwater sea cucumber observation and counting methods, an autonomous unmanned surface vehicle (USV) system for sea cucumber multi-target tracking and counting in pond aquaculture environments was designed. The system adopted a hierarchical and modular architecture, enhancing flexibility, stability, and maintainability. The hardware included a hull, an underwater perception system, and an onboard control box, featuring a quick-release and foldable design with a swivel buckle mechanism for easy transportation and installation. Additionally, the system integrated an autonomous underwater video capture module, combined with altitude-holding and trajectory-tracking algorithms to ensure stable and autonomous video acquisition. A sea cucumber detection and counting algorithm based on YOLO v10 and ByteTrack enabled fast and accurate counting. Experimental results demonstrated that the USV and underwater perception module remained stable under Level 3 wind and waves, with a pitch angle range of - 18° to 18°. The counting algorithm achieved a normalized mean absolute error (NMAE) of 0. 111 1, while the trajectory planning maintained an average error of 0. 47 m. The altitude-holding algorithm converged within 10 s. Field tests in aquaculture environments confirmed that the system was stable and reliable, meeting the daily operational requirements of pond aquaculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Unmanned surface vehicles

Controlled terms: Autonomous underwater vehicles? - ?Fish ponds? - ?Lakes? - ?Target tracking? - ?Underwater acoustics

Uncontrolled terms: Hierarchical architectures? - ?High costs? - ?High-low? - ?Multi-target-tracking? - ?Pond aquacultures? - ?Sea cucumber? - ?Surface vehicles? - ?Target counting? - ?Unmanned surface vehicle for in-situ sea cucumber counting? - ?Unmanned surface vehicle systems

Classification code: 407 Maritime and Port Structures; Rivers and Other Waterways? - ?435.2 Tracking and Positioning? - ?444.1 Surface Water? - ?674.1 Small Marine Craft? - ?751.1 Acoustic Waves? - ?821.4 Agricultural Methods

Numerical data indexing: Size 4.70E+01m, Time 1.00E+01s

DOI: 10.6041/j.issn.1000-1298.2025.10.008

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

33. Yield Prediction of Winter Wheat Using Multi-temporal GF 1 Images and Machine Learning

Accession number: 20254619493645

Title of translation: 基于多时相 GF 1 影像和机器学习的冬小麦估产研究

Authors: Lu, Bihui (1); Li, Weiguo (1); Tian, Miao (1); Wang, Jing (1); Mao, Xing (1)

Author affiliation: (1) Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing; 210014, China

Corresponding author: Li, Weiguo(jaaslwg@126.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 448-457

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Crop yield estimation is crucial for formulating agricultural policies and ensuring regional and national food security. Multi-temporal vegetation indices (VIs) extracted from GF 1 WFV images were used to characterize the growth characteristics of winter wheat. A quantitative analysis of the correlation between different VIs across multiple growth periods and winter wheat yield was conducted. Based on the results of correlation analysis, vegetation index features were selected. Three machine learning algorithms, including back propagation neural network (BPNN), support vector regression (SVR), and random forest regression (RFR), were used to construct winter wheat yield prediction models, and accuracy evaluation was conducted. The results showed that the correlation between VIs and yield gradually increased as the growth period advanced, with the highest correlation (up to 0. 7) between VIs and yield occurring during the booting period on March 26th. The modeling method based on the combination of multi-temporal VIs effectively improved the estimation accuracy of winter wheat yield. The combination of VIs during the jointing and booting stages produced higher estimation accuracy than the use of VIs at a single growth stage. The BPNN model estimated a determination coefficient R2 that increased by about 0. 104, the SVR model R2 increased by about 0. 141, and the RFR model R2 increased by about 0. 107. For the multi-temporal VIs during the booting stage, all three models demonstrated high accuracy. The highest estimation accuracy of the BPNN model was 91. 47% with R2 of 0. 859 and RMSE of 472. 873 kg/ hm2, while the highest estimation accuracy of the SVR model was 91. 03% with R2 of 0. 826 and RMSE of 492. 917 kg/ hm2. The highest accuracy of the RFR model was 92. 25% with R2 of 0. 908 and RMSE of 445. 874 kg/ hm2. Compared with the traditional linear regression model, the accuracy of the optimal models based on three machine learning algorithms improved by 7. 53, 7. 09, and 8. 31 percentage points, respectively. The research results provided a reference for winter wheat yield prediction based on multi-temporal domestic high-resolution satellite data. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 42

Main heading: Learning systems

Controlled terms: Agricultural machinery? - ?Backpropagation? - ?Crops? - ?Food supply? - ?Forecasting? - ?Linear regression? - ?Neural networks? - ?Prediction models? - ?Support vector regression? - ?Vegetation

Uncontrolled terms: Back-propagation neural networks? - ?GF 1? - ?Growth period? - ?Machine-learning? - ?Multi-temporal? - ?Random forests? - ?Vegetation index? - ?Winter wheat? - ?Winter wheat yields? - ?Yield prediction

Classification code: 101.1 Biomedical Engineering? - ?103 Biology? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products? - ?822.3 Food Products? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1202 Statistical Methods? - ?1202.2 Mathematical Statistics

Numerical data indexing: Mass 8.73E+02kg, Mass 8.74E+02kg, Mass 9.17E+02kg, Percentage 2.50E+01%, Percentage 3.00E+00%, Percentage 4.70E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.039

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

34. Exploration and Practice of Unmanned Smart Rice Farms

Accession number: 20254619501311

Title of translation: 水稻无人化智慧农场探索与实践

Authors: Luo, Xiwen (1, 2); Hu, Lian (1, 2); Liao, Juan (3, 4); Zhou, Zhiyan (3, 4); He, Jie (1, 2); Ma, Xu (4); Zhang, Wenyu (3, 4); Zeng, Shan (3, 4)

Author affiliation: (1) Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou; 510642, China; (2) State Key Laboratory of Agricultural Equipment Technology, Beijing; 100083, China; (3) Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou; 510642, China; (4) College of Engineering, South China Agricultural University, 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: 10

Issue date: October 2025

Publication year: 2025

Pages: 277-290

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The exploration and practice of South China Agricultural University in building unmanned rice smart farms were introduced, focusing on four key technologies: digital perception technology: precisely sensing information about the operational environment, objects, and machinery in unmanned farms; intelligent decision-making technology: intelligent decision-making for land reclamation, tillage, planting, sowing, field management, and harvesting; precision operation technology: precision operations for agricultural machinery navigation and targeted farming tasks; smart management technology: smart management for crop production, machinery maintenance, and farm operations. It achieved five functions, including full coverage production links of tillage, planting, field management, and harvesting; automatically transferred from garage and field; automatically obstacle avoidance and parking for safety; real-time monitoring of crop production process and intelligent decision-making precision operation. Remarkable economic, social and ecological benefits were achieved in this unmanned rice farm. By the end of 2024, more than 30 unmanned farms were built in 16 provinces in China, involving crops of rice, wheat, corn and peanuts, soil types of paddy fields and drylands, operation stages of plowing, planting, management, harvesting, and machinery modes of single-machine and multimachine. In 2021, the yield of Simiao rice variety in the unmanned rice farm in Zengcheng, Guangdong, was 9 934. 35 kg/ hm2, 32% more than the local average yield of the same rice variety. In 2023, the yield of ratoon rice in unmanned farm in Qianshanhong Town of Yiyang, Hunan Province was 18 625. 5 kg/ hm2 in two seasons, which demonstrated that crops can be planted well in unmanned farm without people. Notably, it set new high-yield records for rice, demonstrating that farming can be done without human in the fields, and even achieve high productivity. The research result can provide a critical solution to the challenges of “who will farm” and “how to farm”. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Harvesting

Controlled terms: Agricultural machinery? - ?Automation? - ?Crops? - ?Cultivation? - ?Decision making? - ?Engineering education? - ?Engineering research? - ?Land reclamation? - ?Navigation? - ?Precision agriculture ? - ?Smart agriculture? - ?Tillage

Uncontrolled terms: Crop production? - ?Field management? - ?Intelligent agricultural machinery? - ?Intelligent decision-making? - ?Modern agricultures? - ?Plantings? - ?Rice? - ?Smart agricultures? - ?Smart farm? - ?Unmanned farm

Classification code: 435.1 Navigation? - ?442 Flood Control; Land Reclamation? - ?731 Automatic Control Principles and Applications? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?901.2 Education? - ?901.3 Engineering Research? - ?912.2 Management? - ?1502.4 Biodiversity Conservation

Numerical data indexing: Mass 3.50E+01kg, Mass 5.00E+00kg, Percentage 3.20E+01%, Size 5.08E-02m

DOI: 10.6041/j.issn.1000-1298.2025.10.023

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

35. Design and Experiment of Phenotypic Acquisition Device Based on Ear Self-rotation and Single-camera Observation

Accession number: 20254619493736

Title of translation: 基于果穗自转与单相机观测的表型采集装置设计与试验

Authors: Sun, Bo (1); Chen, Hongming (1); Zhao, Leilei (1); Wang, Xuerui (1); Zhu, Xueting (1); Huangfu, Yi (1); Zhang, Hongfu (1); Yang, Linlin (1)

Author affiliation: (1) College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming; 650000, China

Corresponding author: Yang, Linlin(kgy200398@126.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 492-499

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of high costs, complex equipment structure, incomplete sample information extraction, and cumbersome operation associated with traditional maize ear phenotype collection equipment, a phenotype collection scheme utilizing ear rotation and a single camera for targeted observation were proposed. A device for collecting maize ear phenotypes was designed, featuring an upright structure, multiple workstations, and synchronous data collection. This device can accommodate 10 maize ears, with a stepper motor driving a rotating platform to capture images of the ears sequentially, while a synchronous belt driving the rotation of the ten workstations to complete the phenotype collection for each individual ear. The enclosed frame eliminated external interference and maintained uniform illumination; the camera was installed 235 mm away from the maize ear to capture phenotype parameters via video stream. A panoramic image stitching algorithm was used to unfold and identify the number of rows and kernels on the ear, while measurements of ear length and thickness were taken from side images of the ear. Experimental results showed that the average time taken for phenotype collection per ear by using the ear rotation and single camera observation device was 38. 15 s, with measurement accuracy for ear length at 98. 89%, ear thickness at 97. 10%, row count accuracy at 97. 31%, and kernel count accuracy at 96. 19% . ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 21

Main heading: Rotation

Controlled terms: Belt drives? - ?Cameras? - ?Data acquisition? - ?Grain (agricultural product)

Uncontrolled terms: Acquisition device? - ?Collection schemes? - ?Complex equipment? - ?Data collection? - ?High costs? - ?Maize ears? - ?Phenotype? - ?Sample information? - ?Self rotations? - ?Single cameras

Classification code: 602.2 Mechanical Transmissions? - ?742.2 Photographic and Video Equipment? - ?821.5 Agricultural Products? - ?1106.2 Data Handling and Data Processing? - ?1301.1.1 Mechanics

Numerical data indexing: Percentage 1.00E+01%, Percentage 1.90E+01%, Percentage 3.10E+01%, Percentage 8.90E+01%, Size 2.35E-01m, Time 1.50E+01s

DOI: 10.6041/j.issn.1000-1298.2025.10.043

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

36. Estimation of Forest Stock Volume in Greater Khingan Mountains Based on MCMC Joint Multi-model Framework

Accession number: 20254619493671

Title of translation: 基于 MCMC 联合多模型框架的大兴安岭地区森林蓄积量估算

Authors: Sun, Xiangnan (1, 2); Pang, Yong (1); Zeng, Weisheng (2); Chen, Xinyun (2); Xie, Yunhong (3); Wang, Cangjiao (1)

Author affiliation: (1) Institute of Resource Information, Chinese Academy of Forestry, Beijing; 100091, China; (2) Institute of Forest and Grassland Inventory and Planning, National Forestry and Grassland Administration, Beijing; 100714, China; (3) National Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management, Beijing Forestry University, Beijing; 100083, China

Corresponding author: Pang, Yong(pangy@ifrit.ac.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 458-469

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to accurately estimate forest stock in the Greater Khingan Range region, selecting Sentinel ? 1 and Sentinel ? 2 images, combined with geographical and climatic factors, and the Boruta method was used to screen independent variables. Totally eight machine learning models, including random forest (RF), gradient boosting machine (GBM), lightweight gradient boosting machine (LGBM), eXtreme gradient boosting (XGB), adaptive boosting (ADB), extremely randomized trees (ETR), K-nearest neighbors (KNN), and support vector machine (SVM), were used to regress forest stock at the sample scale. The Markov chain Monte Carlo (MCMC) joint multi model framework algorithm was used to combine multiple models and optimize the combination weights for testing, and predict the dataset and generate joint multi model prediction results. The results showed that the ETR performed the best in single model prediction, with Pearson correlation coefficient r, determination coefficient R2, root mean square error (RMSE), and mean relative error (MRE) mean values of 0. 752, 0. 584, 1. 884 m3, and 29. 3%, respectively, obtained through ten-fold cross-validation. In the joint model, the joint R2 of the five models (LGBM + XGB + ADB + ETR + SVM) and the six models (RF + LGBM + XGB + ADB + ETR + SVM) was the highest (0. 62), the RMSE was the lowest, and the joint R2 improvement of the two models was the highest (0. 037). According to the judgment of R2 and RMSE, the overall performance of the combined model was better than that of the single model, showing higher prediction accuracy and stability. The use of multi-source data and MCMC combined with a multi model framework can effectively improve the estimation accuracy of forest stock in the Greater Khingan Range region, provide scientific data support for forest resource management, and can be widely applied to the prediction and simulation of other forest ecological parameters. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Mean square error

Controlled terms: Adaptive boosting? - ?Chains? - ?Correlation methods? - ?Data accuracy? - ?Decision trees? - ?Forecasting? - ?Forest ecology? - ?Intelligent systems? - ?Learning systems? - ?Markov chains ? - ?Monte Carlo methods? - ?Natural resources management? - ?Nearest neighbor search? - ?Prediction models? - ?Random forests? - ?Reforestation? - ?Remote sensing? - ?Scales (weighing instruments)? - ?Statistical tests? - ?Support vector machines

Uncontrolled terms: Forest stock volume? - ?Gradient boosting? - ?Machine-learning? - ?Markov chain Monte Carlo? - ?Markov chain monte carlo algorithms? - ?Markov Chain Monte-Carlo? - ?Multi-modelling? - ?Multi-source remote sensing? - ?Multi-Sources? - ?Remote-sensing

Classification code: 601.2 Machine Components? - ?602.1 Mechanical Drives? - ?731.1 Control Systems? - ?821.1 Woodlands and Forestry? - ?942.1.7 Special Purpose Instruments? - ?961 Systems Science? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1106.2 Data Handling and Data Processing? - ?1201.5 Computational Mathematics? - ?1201.7 Optimization Techniques? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1202 Statistical Methods? - ?1202.1 Probability Theory? - ?1202.2 Mathematical Statistics? - ?1501.2.1 Resource Conservation? - ?1502.2 Ecology and Ecosystems? - ?1502.4 Biodiversity Conservation

Numerical data indexing: Percentage 3.00E+00%, Size 8.84E+02m

DOI: 10.6041/j.issn.1000-1298.2025.10.040

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

37. Improved A* Algorithm Path Planning Based on Skeleton Channel and Skeleton Potential Field

Accession number: 20254619496467

Title of translation: 基于骨架通道与势场的改进 A* 算法路径规划

Authors: Tang, Bo (1, 2); Liu, Yi (1); Lei, Bin (1, 2); Hu, Yuxin (3); Li, Yunfei (1); Jiang, Lin (1, 2)

Author affiliation: (1) Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan; 430081, China; (2) Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan; 430081, China; (3) Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan; 430081, China

Corresponding author: Jiang, Lin(jianglin76@wust.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 625-634 and 757

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: At present，many existing path planning methods are difficult to take both efficiency and safety simultaneously into account in robot navigation． In order to solve this problem，an improved A* algorithm based on skeleton channel and skeleton potential field was proposed，which aimed to efficiently complete the navigation task under the premise of ensuring the safety of the robot． In the first place，the approach began with a multi-step preprocessing of the grid map to extract the skeleton and generate a global skeleton map． Based on the given start and goal points，an initial path was searched on the skeleton，and the points surrounding the initial path were expanded to form a skeleton channel． In the second place，a skeleton potential field function was then constructed to calculate the potential field values within the skeleton channel，which were subsequently integrated into the cost function of the A* algorithm． Within the skeleton channel，the A* algorithm，enhanced by the improved cost function，was employed to search for the optimal path． In the end，the path was smoothed by using a third-order Bézier curve． In multiple simulations and real-world navigation experiments，the proposed algorithm was compared with a Voronoi diagram-based algorithm that featured improved skeleton extraction， as well as the standard A* algorithm． The results demonstrated that the proposed approach can plan a reasonable navigation path，allowing the robot to reach the target point safely and efficiently. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Motion planning

Controlled terms: Air navigation? - ?Computational geometry? - ?Cost functions? - ?Extraction? - ?Graphic methods? - ?Musculoskeletal system? - ?Robot programming? - ?Three dimensional computer graphics

Uncontrolled terms: A* algorithm? - ?Bezier curve? - ?Cost-function? - ?Improved A* algorithm? - ?Path planning method? - ?Potential field? - ?Skeleton channel? - ?Skeleton potential field? - ?Third order? - ?Third-order bezy curve

Classification code: 101.4 Biomechanics, Bionics and Biomimetics? - ?435.1.1 Air Navigation and Traffic Control? - ?731.5 Robotics? - ?802.3 Chemical Operations? - ?902.1 Engineering Graphics? - ?1101 Artificial Intelligence? - ?1106.1 Computer Programming? - ?1106.2 Data Handling and Data Processing? - ?1201.5 Computational Mathematics? - ?1201.7 Optimization Techniques? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1201.14 Geometry and Topology

DOI: 10.6041/j.issn.1000-1298.2025.10.057

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

38. Food Sensory Analysis Based on Fine-grained Aspect-level Opinion Extraction

Accession number: 20254619506397

Title of translation: 基于细粒度方面级意见提取的食品感官分析研究

Authors: Tian, Xuan (1); Li, Aohan (1); Wang, Tiantian (1); Zhu, Baoqing (2)

Author affiliation: (1) School of Information Science and Technology, Beijing Forestry University, Beijing; 100083, China; (2) School of Biological Sciences and Technology, Beijing Forestry University, Beijing; 100083, China

Corresponding author: Zhu, Baoqing(zhubaoqing@bjfu.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 716-724

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the field of food research，text reviews carry rich sensory information，and analyzing these text reviews can help to better mine consumer preferences and experiences． At present，manual analysis of text reviews usually requires a lot of time and effort，and the subjective tendencies of evaluating reviewers also affect the final sensory analysis results． In order to solve such problems，a fine grain sensory analysis model，i． e．，fine-grained sensory analysis model with opinion intensity （ FGSAM－OI）was proposed based on the aspect-level opinion extraction method． The model was designed to effectively extract sensory terms and their corresponding intensity levels from text reviews regarding specific aspects of food，based on deep learning，to accurately obtain consumers’ sensory experiences of the food． Firstly，an intensity attention mechanism was designed in FGSAM－OI to enhance the representation capability of intensity terms in the input sequence． Secondly， to further associate intensity terms with the corresponding sensory terms，an intensity syntactic tree was designed to learn the syntactic relationships within review texts，thereby more accurately capturing the connection between sensory terms and intensity terms，and thus improving the overall sensory analysis effect for various aspects of the food． The experimental results showed that the addition of intensity attention mechanism and intensity syntax tree can improve the extraction precision of sensory terms and sensory intensity by 3. 73 and 5. 1 percentage points，respectively，and effectively improve the sensory analysis ability of fine grain food review texts. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Syntactics

Controlled terms: Deep learning? - ?Extraction? - ?Food chemistry? - ?Forestry? - ?Sensory analysis? - ?Sensory perception

Uncontrolled terms: Analysis models? - ?Attention mechanisms? - ?Fine grained? - ?Fine-grained sensory analyze model? - ?Finer grains? - ?Food research? - ?Food sensory evaluation? - ?Opinion extraction? - ?Sensory information? - ?Syntax tree

Classification code: 101.5 Ergonomics and Human Factors Engineering? - ?802.3 Chemical Operations? - ?821.1 Woodlands and Forestry? - ?822 Food Technology? - ?1101.2.1 Deep Learning

DOI: 10.6041/j.issn.1000-1298.2025.10.065

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

39. UWB Localization Error Mitigation Using Chan－Taylor Estimation and Optimized Particle Filter

Accession number: 20254619496440

Title of translation: 基于 Chan－Taylor 估计与优化粒子滤波的 UWB 定位误差抑制方法研究

Authors: Wang, Faan (1, 2); Zhu, Shiliang (1, 2); Zhang, Zhaoguo (1, 2); Jia, Mengnan (1); Liang, Jinhao (3); Lu, Yanbo (4); Liu, Ying (4); Shen, Cheng (5)

Author affiliation: (1) Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming; 650500, China; (2) Research Center on Mechanization Engineering, Chinese Medicinal Materials of Yunnan Universities, Kunming; 650500, China; (3) School of Mechanical Engineering, Southeast University, Nanjing; 211189, China; (4) School of Vehicle and Mobility, Tsinghua University, Beijing; 100084, China; (5) Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing; 210014, China

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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 614-624

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenges of ultra-wide band （ UWB ） positioning accuracy in agricultural greenhouse environments，which are degraded by non-line-of-sight （ NLOS） effects and multipath interference，a novel localization algorithm integrating Chan－Taylor （ CT） and improved sand cat swarm intelligence optimization particle filter （ CT+ISCSO－PF） was proposed． Firstly，the Chan－Taylor algorithm was employed to rapidly estimate the target’s initial position，providing an accurate starting value for particle filtering． Subsequently，the improved sand cat swarm optimization （ ISCSO）mechanism was introduced to guide particles toward high-likelihood regions，enhancing global search capabilities through a triangular walk strategy and improving local convergence efficiency via a Levy flight mechanism，thereby effectively suppressing particle degeneracy． Simulations under three distinct noise levels demonstrated that the CT+ISCSO－PF algorithm outperformed traditional particle filter （ PF），CT+ PF （ Chan－Taylor and particle filter），CT+SCSO－PF （ Chan－Taylor and sand cat swarm optimization particle filter），and CT + GWO － PF （ Chan － Taylor and grey wolf optimizer particle filter） across all scenarios． Further real-world validation using agricultural tracked vehicles in greenhouse environments revealed that，under line-of-sight （ LOS） conditions，the proposed algorithm reduced root mean square error （ RMSE） by 27. 9%，17. 8%，7. 8%，and 10. 2% compared with that of PF，CT+PF，CT+SCSO－PF，and CT+GWO－PF，respectively． Under NLOS scenarios，the RMSE reductions reached 21. 4%，15. 6%，7. 6%，and 5. 2%，confirming the algorithm’s robustness and precision in both LOS and NLOS environments. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Greenhouses

Controlled terms: Agriculture? - ?Global optimization? - ?Sand? - ?Swarm intelligence? - ?Ultra-wideband (UWB)

Uncontrolled terms: Chan － taylor? - ?Greenhouse environment? - ?Improved sand cat swarm intelligence optimization algorithm? - ?Particle filter? - ?Swarm intelligence optimization algorithm? - ?Swarm optimization? - ?Ultra-wide? - ?Ultra-wide band positioning? - ?UWB localization? - ?Wide-band

Classification code: 482.2 Rocks? - ?716.3 Radio Systems and Equipment? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.7 Farm Buildings and Other Structures? - ?1101 Artificial Intelligence? - ?1201.7 Optimization Techniques

Numerical data indexing: Percentage 2.00E+00%, Percentage 4.00E+00%, Percentage 6.00E+00%, Percentage 8.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.056

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

40. Design and Experiment of Chain Tooth-plow Subtidal Bottom-sown Manila Clam Harvester

Accession number: 20254619496425

Title of translation: 链齿-犁式浅海底播菲律宾蛤仔采收机设计与试验

Authors: Wu, Hao (1, 2); Zhang, Guochen (1, 2); Li, Hangqi (1, 2); Li, Xiuchen (2); Zhou, Yuchao (3); Mu, Gang (2)

Author affiliation: (1) College of Engineering, Shenyang Agricultural University, Shenyang; 110866, China; (2) School of Mechanical and Power Engineering, Dalian Ocean University, Dalian; 116023, China; (3) General Technology Group Dalian Machine Tool Co., Ltd., Dalian; 116110, China

Corresponding author: Mu, Gang(mugang@dlou.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 71-81

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: At present, there are problems with high excavation resistance and high breakage rate in the harvesting of shallow sea Manila clams. Therefore, a chain tooth-plow collaborative harvesting scheme was proposed, and a chain tooth harvesting mechanism and a drag reducing loosening plow suitable for shallow sea clam harvesting operations were designed. The motion and force analysis of the harvesting chain teeth were carried out, and the plow body surface was designed based on the horizontal straight line method. A discrete element simulation study was conducted on the process of reducing drag and loosening soil plow and collaborative harvesting operations. Single factor experiments and Box Behnken experiments were used to optimize the width, cutting angle, and installation angle of reducing drag and loosening soil plow. A chain tooth-plow collaborative harvesting simulation experiment was also conducted. The results showed that when the width of the reducing drag and loosening soil plow was 192 mm, the installation angle was 37. 81°, and the cutting angle was 25. 84°, the plow body resistance was the smallest, which was 37. 81 N. When the distance between plow teeth was 30. 6 cm, the distance between chain teeth was 33. 96 mm, and the plow depth was 30 mm, the excavation resistance of the chain teeth was the smallest, which was 420. 57 N, and the harvesting rate was 88. 43% . Based on the optimal parameters obtained from simulation, a chain tooth-plow collaborative clam harvesting machine was processed, and harvesting experiments were conducted. Compared with the chain toothed harvesting machine, the chain tooth-plow collaborative harvesting equipment reduced excavation resistance by 55. 35%, increased recovery rate by 23. 7%, reduced sand content by 28. 45%, and reduced damage rate by 25. 58% . The chain tooth-plow collaborative harvesting equipment improved the efficiency of clam harvesting, reduced excavation resistance, and decreased the sand content and crushing rate of shellfish. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Shellfish

Controlled terms: Aquaculture? - ?Chains? - ?Cutting? - ?Cutting equipment? - ?Drag reduction? - ?Excavation? - ?Harvesters? - ?Harvesting? - ?Molluscs? - ?Ocean structures

Uncontrolled terms: Chain tooth plow collaborative harvester? - ?Clam harvesting? - ?Cutting angles? - ?Discrete-element simulations? - ?Harvesting operations? - ?Manila clam? - ?Parameter optimization? - ?Sea bottom? - ?Shallow sea? - ?Shallow sea bottom-sown aquaculture

Classification code: 103 Biology? - ?301.1.3 Aerodynamics (fluid flow)? - ?405.2 Construction Methods? - ?471 Marine Science and Oceanography? - ?472 Ocean Engineering? - ?601.2 Machine Components? - ?602.1 Mechanical Drives? - ?604.1 Metal Cutting? - ?651 Aerodynamics? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?942.2 Miscellaneous Devices, Equipment and Components

Numerical data indexing: Force 5.70E+01N, Force 8.10E+01N, Percentage 3.50E+01%, Percentage 4.30E+01%, Percentage 4.50E+01%, Percentage 5.80E+01%, Percentage 7.00E+00%, Size 1.92E-01m, Size 3.00E-02m, Size 6.00E-02m, Size 9.60E-02m

DOI: 10.6041/j.issn.1000-1298.2025.10.007

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

41. Review on Agricultural Big Data and Privacy Computing Technology

Accession number: 20254619496447

Title of translation: 农业大数据与隐私计算技术研究综述

Authors: Wu, Zhengxian (1); Wen, Juan (1)

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

Corresponding author: Wen, Juan(wenjuan@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: 10

Issue date: October 2025

Publication year: 2025

Pages: 184-199 and 276

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In recent years, with the rapid development of sensors, remote sensing, and Internet technologies, agricultural big data has shown explosive growth. The traditional centralized processing methods integrated multi-party agricultural data for crop growth evaluation and yield prediction. However, these methods face great challenges with privacy leakage and low sharing efficiency in the process of data transmission. Moreover, multi-party data also faces data security issues such as data copyright protection. To address the above problems, privacy computing, an emerging data security technology, provides a feasible path to achieve multi-party collaborative analysis without disclosing the original data. Firstly, the landscape of agricultural big data was comprehensively reviewed from data acquisition, preprocessing, storage, and analysis to its key applications in precision farming, yield forecasting, pest monitoring, supply chain traceability, and agricultural finance. Then the principles and applicable scenarios of mainstream privacy computing technologies such as homomorphic encryption, secure multi-party computation, differential privacy, and federated learning were systematically summarized. To emphatically analyze the applications of privacy computing technology in the agricultural field, the advanced research of privacy computing was outlined in agricultural data transmission, pest and disease detection, crop monitoring, and yield prediction. Meanwhile, existing key challenges of the current privacy computing technology were summarized, including communication cost, computational complexity, heterogeneous adaptability, evaluation mechanism, and the trade-off between privacy protection and model performance. Finally, future directions of privacy computing technologies on communication compression, lightweight encryption, multimodal modeling, construction of evaluation systems, and privacy-performance optimization were explored to facilitate the intelligent development of agriculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 95

Main heading: Economic and social effects

Controlled terms: Agricultural economics? - ?Computer privacy? - ?Copyrights? - ?Crops? - ?Data communication systems? - ?Data Sharing? - ?Differential privacy? - ?Digital storage? - ?Privacy-preserving techniques? - ?Smart agriculture ? - ?Technological forecasting

Uncontrolled terms: Agricultural big data? - ?Centralized processing? - ?Computing technology? - ?Data-transmission? - ?Explosive growth? - ?Internet technology? - ?Privacy computing? - ?Remote sensing technology? - ?Smart agricultures? - ?Yield prediction

Classification code: 821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?901.4 Impact of Technology on Society? - ?902.3 Legal Aspects? - ?911.2 Industrial Economics? - ?971 Social Sciences? - ?1101.2 Machine Learning? - ?1103.1 Data Storage, Equipment and Techniques? - ?1103.3 Data Communication, Equipment and Techniques? - ?1105 Computer Networks? - ?1106 Computer Software, Data Handling and Applications? - ?1106.2 Data Handling and Data Processing? - ?1108.1 Cybersecurity? - ?1108.2 Cryptography

DOI: 10.6041/j.issn.1000-1298.2025.10.019

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

42. Path Following Control and Experiments for Automatic Feeding Robots in Recirculating Aquaculture Systems

Accession number: 20254619496427

Title of translation: 循环水养殖自动投喂机器人路径跟踪控制与试验

Authors: Xia, Yingkai (1, 2); Guo, Zhengjiang (1); Liu, Jiajun (1); Gao, Jian (2, 3); Wan, Peng (1, 2)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 130-139

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 labor intensity and significant labor costs in feed delivery within recirculating aquaculture systems, an automatic feeding robot was designed. Furthermore, a physics-informed neural network based model predictive control (PINN-MPC) method was proposed to tackle the autonomous path tracking problem under variable payloads and slippery road conditions. Firstly, the overall robot architecture and path planning control scheme were designed. Secondly, a control model was established for the robot under variable payloads and complex environment. Subsequently, building upon the traditional MPC framework, the proposed method treated key physical parameters as time-varying factors, and a multi-layer feedforward neural network was employed to predict these parameters online, enhancing control precision. Finally, the effectiveness of the control algorithm was validated through simulations and field experiments. In the single-tank feeding experiment, the average tracking errors of the PINN-MPC at two key observation points were 0. 12 m and 0. 18 m, representing a 50% reduction compared with MPC. The longitudinal velocity fluctuation was half of that of MPC, and the standard deviation of lateral deviation was decreased by 58. 3% . In the multi-tank feeding experiment, PINN-MPC maintained the average path error between the nine target points within 0. 050 ~ 0. 055 m, reduced lateral tire force fluctuation by 58. 9% . ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Model predictive control

Controlled terms: Aquaculture? - ?Employment? - ?Feeding? - ?Motion planning? - ?Multilayer neural networks? - ?Network layers? - ?Predictive control systems? - ?Robot programming? - ?Tanks (containers)? - ?Wages

Uncontrolled terms: Automatic feeding? - ?Automatic feeding robot? - ?Model-predictive control? - ?Network-based modeling? - ?Neural-networks? - ?Path following control? - ?Physic-informed neural network? - ?Recirculating aquaculture? - ?Recirculating aquaculture system? - ?Variable payload

Classification code: 610.2 Tanks and Accessories? - ?691.2 Materials Handling Methods? - ?731.1 Control Systems? - ?731.5 Robotics? - ?821.4 Agricultural Methods? - ?901 Engineering Profession? - ?912.3 Personnel? - ?1101 Artificial Intelligence? - ?1105 Computer Networks? - ?1106.1 Computer Programming? - ?1201.7 Optimization Techniques

Numerical data indexing: Percentage 3.00E+00%, Percentage 5.00E+01%, Percentage 9.00E+00%, Size 1.20E+01m, Size 1.80E+01m, Size 5.50E+01m

DOI: 10.6041/j.issn.1000-1298.2025.10.013

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

43. Unmanned Weighing System for Catfish Based on Machine Vision and Multi-sensor Collaboration

Accession number: 20254619493437

Title of translation: 基于机器视觉与多传感器协同的鮰鱼无人称量系统研究

Authors: Xiao, Maohua (1); Ji, Shuying (1); Zhu, Hong (2); Li, Dongfang (1); Wang, Bingqing (3)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 210031, China; (2) Jiangsu Agricultural Machinery Development and Application Center, Nanjing; 210017, China; (3) Jiangsu Agricultural Machinery Information Center, Nanjing; 210017, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 45-53 and 81

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The scale of China’s aquaculture industry continues to expand, but the traditional method of weighing catfish exists in low efficiency, large error, serious fish stress damage and other problems. A catfish unmanned weighing system was proposed based on machine vision and multi-sensor synergy, fusing target detection algorithms and multi-source information measurement technology. Through the improved YOLO 11 model (HY ? YOLO 11), deformable convolution (DCNv3) and spatial enhancement attention module (SEAM) were introduced to effectively solve the problem of fish sticking together, cage deformation and light fluctuation interference in dynamic scenes. The system integrated a floating monitoring platform, an industrial camera and a tensile force sensor, combined with an adaptive lifting mechanism and a buoyancy compensation mechanism, to realize the simultaneous and accurate measurement of the total mass and quantity of the fish. The experimental results showed that the average detection accuracy (mAP50) of the HY ? YOLO 11 model reached 94. 8% in the dense occlusion scenario, which was 3. 7 percentage points higher than the baseline model, the average absolute error (MAE) of fish counting was 0. 56, the relative error of mean weight calculation was less than 4%, and average time for a single weighing session was 58 s. The system provides an efficient and reliable technical solution for the intelligent management of aquaculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 33

Main heading: Computer vision

Controlled terms: Agricultural machinery? - ?Aquaculture? - ?Errors? - ?Fish? - ?Machine vision? - ?Scales (weighing instruments)? - ?Weighing

Uncontrolled terms: Catfish? - ?Contact less? - ?Contactless weighing system? - ?DCNv3? - ?Machine-vision? - ?On-machines? - ?Spatial enhancement? - ?Spatial enhancement attention module? - ?Weighing systems? - ?YOLO 11

Classification code: 731.1.1 Error Handling? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?822.3 Food Products? - ?942.1.7 Special Purpose Instruments? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 4.00E+00%, Percentage 8.00E+00%, Time 5.80E+01s

DOI: 10.6041/j.issn.1000-1298.2025.10.004

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

44. Agricultural Question Classification by Fusing Topic Features with Domain Pre-trained Models

Accession number: 20254619496455

Title of translation: 主题特征与领域预训练模型融合的农业问句分类方法

Authors: Xiong, Shufeng (1); Shen, Jialong (1); Xu, Hengrui (1); Wang, Bingkun (2)

Author affiliation: (1) College of Information and Management Science, Henan Agricultural University, Zhengzhou; 450046, China; (2) School of Information Engineering, Zhengzhou College of Finance and Economics, Zhengzhou; 450049, China

Corresponding author: Wang, Bingkun(wangbingkun@zzife.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 606-613

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The accuracy of question classification was crucial for question-answering systems，particularly in the agricultural domain where question categories were diverse and semantic content was complex．Effectively classifying user-submitted questions was a key task in the development of intelligent agricultural question-answering systems． To address the issue of effectively classifying user-submitted questions in the agricultural domain，an agricultural question classification method （ TAB，topic model with agriculture BERT （ AgCBERT） for agricultural text classification） was proposed，combining domain-specific BERT pre-training with topic modeling to improve classification performance． To manage the professional and complex nature of agricultural data， a specialized pre-trained language model，AgCBERT，was developed for text feature vector representation． Additionally，the LDA model was employed to represent topic feature vectors as a supplement to the text vectors． The TAB method linearly combined these two types of features and input them into a fully connected network for classification prediction． The efficacy of this approach was underscored by experimental results obtained from a real-world agricultural question dataset． The method achieved F1 score of 72. 72%，which was substantially higher than those achieved by commonly used text classification models， indicating a significant improvement in classification performance． The results demonstrated that the TAB method， which integrated AgCBERT with a topic probability model，excelled in agricultural question classification． This integration allowed for a more nuanced understanding and processing of complex question types，leading to more accurate classifications. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 39

Main heading: Complex networks

Controlled terms: Agribusiness? - ?Agriculture? - ?Artificial intelligence? - ?Classification (of information)? - ?Learning systems? - ?Modeling languages? - ?Question answering? - ?Semantics? - ?Smart agriculture? - ?Statistics ? - ?Text processing

Uncontrolled terms: Agricultural domain? - ?BERT? - ?Classification performance? - ?Features vector? - ?Language model? - ?Pre-trained language model? - ?Question answering systems? - ?Question categories? - ?Question classification? - ?Topic Modeling

Classification code: 716.1 Information Theory and Signal Processing? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.4 Agricultural Methods? - ?903.1 Information Sources and Analysis? - ?903.2 Information Dissemination? - ?903.3 Information Retrieval and Use? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1105 Computer Networks? - ?1106.2 Data Handling and Data Processing? - ?1106.4 Database Systems? - ?1106.7 Computational Linguistics? - ?1202.2 Mathematical Statistics

Numerical data indexing: Percentage 7.20E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.055

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

45. Apple Leaf Disease Detection Method Based on Improved YOLO v8

Accession number: 20254619496413

Title of translation: 基于改进 YOLO v8 的苹果叶片病害检测研究

Authors: Xu, Hongli (1); An, Feng (1); Shi, Yuxuan (2)

Author affiliation: (1) School of Information Science and Engineering, Shandong Agricultural University, Taian; 271018, China; (2) College of Computer Science and Technology, Shandong University of Technology, Zibo; 255000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 530-538

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at the problems of poor real-time performance, low accuracy, false detections, and missed detections in current apple leaf disease target detection algorithms in complex environments, focusing on common diseases such as black rot, gray spot, rust, and scab on apple leaves, an improved YOLO v8-based detection model for apple leaf diseases was constructed. Firstly, the traditional convolution operation (Conv) was replaced with partial convolution (PConv), and based on PConv, the C2f_ Faster module was designed to replace the C2f module in the backbone network, reducing the computational load of the model without compromising its accuracy. Secondly, a triple attention mechanism was added after the spatial pyramid pooling module (SPPF) to enhance feature extraction capabilities in complex backgrounds. Finally, the CIoU loss function in YOLO v8 was replaced with the minimum point distance-based intersection over union loss function MPDIoU to improve the localization accuracy of disease targets. Validation experiments showed that, for a dataset of apple leaf disease images collected in natural scenes, the computational load and parameter count of the optimized network architecture were reduced by 20. 9% and 23%, respectively, compared with the original YOLO v8n baseline model. Meanwhile, precision, recall, and mAP @ 0. 5 was increased by 1. 5, 2. 9, and 2. 2 percentage points, reaching 89. 2%, 91. 4%, and 94. 6%, respectively. Compared with YOLO v3, YOLO v5, YOLO v8n, YOLO v9n, and YOLO v10n models, mAP@ 0. 5 was improved by 3. 5, 3. 0, 2. 2, 2. 4, and 2. 6 percentage points, respectively. The method proposed significantly improved recognition accuracy while maintaining real-time processing performance, providing reliable technical support for the development of edge computing detection systems for apple leaf diseases. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Deep learning

Controlled terms: Complex networks? - ?Convolution? - ?Edge detection? - ?Fruits? - ?Image processing? - ?Network architecture

Uncontrolled terms: Apple leaf? - ?Attention mechanisms? - ?Computational loads? - ?Deep learning? - ?Disease detection? - ?Leaf disease? - ?Loss functions? - ?Percentage points? - ?Triplet attention mechanism? - ?YOLO v8n

Classification code: 716.1 Information Theory and Signal Processing? - ?821.5 Agricultural Products? - ?1101.2.1 Deep Learning? - ?1105 Computer Networks? - ?1105.2 Internet and Web Technologies? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 2.00E+00%, Percentage 2.30E+01%, Percentage 4.00E+00%, Percentage 6.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.047

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

46. Design and Test of In-situ Steering Wheeled Mobile Platform in Elevated Planting Environment

Accession number: 20254619506396

Title of translation: 设施高架种植环境下原地转向轮式移动平台设计与试验

Authors: Xu, Jian (1); Wang, Minghui (1, 2); 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: 10

Issue date: October 2025

Publication year: 2025

Pages: 693-703

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In view of the problems of large steering radius，large tire and power loss and serious damage to ground film covering in the steering process of traditional wheeled chassis，an in-situ steering wheeled mobile platform based on electric jacking rotation was designed． The lifting mechanism was used to complete the lifting of the mobile platform，and then the in-situ rotation of the mobile platform was realized through the rotating mechanism． Firstly，the working principle and structural parameters of key components such as jacking mechanism，rotating mechanism and control system were analyzed and determined． Then，based on the NX－ADAMS co-simulation technology，the parameter combinations of jacking speed，jacking height，rotation speed and descending motor speed during the in-situ steering of the mobile platform were optimized by using the response surface test method． Finally，the in-situ steering wheeled mobile platform was trial-produced，and the verification test was carried out under the optimal combination of parameters． The test results showed that the optimal parameter combination of the in-situ steering operation process of the mobile platform was the speed of the jacking motor of 1. 71 r/s，the jacking height of 40. 00 mm，the rotation speed of 5（ °） /s，and the speed of the descending motor of 3. 00 r/s． The test results showed that under the optimal parameters，the in-situ steering process of the mobile platform took 33. 08 s，the average force on the ground mulching was 32. 20 kPa，compared with the 345. 46 kPa of the traditional steering mode，it was reduced by 90. 68%，which realized the efficient in-situ steering and film non-destructive． The research result can provide theoretical and technical support for the in-situ steering operation of agricultural robot mobile platform. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Rotation

Controlled terms: Agricultural robots? - ?Drilling platforms? - ?Electric losses? - ?Electric machine control? - ?Jacks? - ?Rotating machinery? - ?Simulation platform

Uncontrolled terms: Design and tests? - ?Facility elevated mode? - ?In-situ steering? - ?Mobile platform? - ?NX － ADAMS simulation? - ?Rotating mechanisms? - ?Rotation speed? - ?Steering operations? - ?Test? - ?Wheeled mobile platform

Classification code: 511 Oil Field Equipment and Production Operations? - ?601.1 Mechanical Devices? - ?605.2 Small Tools, Unpowered? - ?674.2 Marine Drilling Rigs and Platforms? - ?704.2 Electric Equipment? - ?731.2 Control System Applications? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?1009.1 Energy Conservation? - ?1201.12 Modeling and Simulation? - ?1301.1.1 Mechanics

Numerical data indexing: Percentage 6.80E+01%, Pressure 2.00E+04Pa, Pressure 4.60E+04Pa, Size 0.00E00m, Time 8.00E+00s

DOI: 10.6041/j.issn.1000-1298.2025.10.063

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

47. Non-destructive Measurement Technique for Pumpkin Seedling Leaf Area Based on Depth Image Occlusion Completion

Accession number: 20254619496452

Title of translation: 基于深度图像遮挡补全的南瓜幼苗叶面积无损测量技术

Authors: Xu, Shengyong (1, 2); Hu, Shiling (1, 2); Guo, Zhengxiao (1, 2); Wu, Sixiao (1, 2); Bie, Zhilong (3, 4); Hu, Jiakui (5); Huang, Yuan (3, 4)

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; (3) College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan; 430070, China; (4) National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops, Huazhong Agricultural University, Wuhan; 430070, China; (5) Xizang Detang Agriculture and Animal Husbandry Industry Development Co., Ltd., Shannan; 856000, China

Corresponding author: Huang, Yuan(huangyuan@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: 10

Issue date: October 2025

Publication year: 2025

Pages: 539-548 and 574

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the non-destructive detection of leaf area, unavoidable occlusions can lead to significant measurement errors. Full three-dimensional reconstruction or single-view completion are effective methods to address occlusion issues. A method for leaf occlusion completion and non-destructive measurement of leaf area was proposed based on the D-Cycle GAN network. An image acquisition device and software were designed by using the Azure Kinect camera to capture high-resolution D-RGB aligned images. The Mask R-CNN network was utilized for instance segmentation of RGB images to obtain masks for cotyledons and true leaves, thereby segmenting 16-bit depth maps of individual leaves from D-RGB aligned images. The Cycle GAN network was improved by adding unique input-output modules, enabling it to complete 16-bit leaf depth images. Additionally, a region of interest (ROI) cropping method was adopted to effectively avoid resolution loss due to the small size of leaves during image scaling. The completed leaf depth images were processed through point cloud generation, preprocessing, and triangulation to calculate leaf area. Experimental results showed that compared with manual measurements, point cloud completion, and RGB completion methods, the proposed method achieved high precision (R2 =0. 968) and speed (3. 63 s per plant) in leaf area detection across different growth stages of seedlings, balancing accuracy and throughput well, and demonstrating the best overall performance with good application potential. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Seed

Controlled terms: Chemical detection? - ?Forestry? - ?Image acquisition? - ?Image enhancement? - ?Image segmentation? - ?Plants (botany)? - ?Surveying? - ?Three dimensional computer graphics

Uncontrolled terms: Cycle GAN? - ?Depth image? - ?Depth image completion? - ?Image completion? - ?Leaf area? - ?Leaf area measurements? - ?Non-destructive measurement? - ?Occlusion completion? - ?Pumpkin seedling? - ?Seedling

Classification code: 103 Biology? - ?405.3 Surveying? - ?802 Chemical Apparatus and Plants; Unit Operations; Unit Processes? - ?821.1 Woodlands and Forestry? - ?821.5 Agricultural Products? - ?902.1 Engineering Graphics? - ?1106 Computer Software, Data Handling and Applications? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing

Numerical data indexing: Time 6.30E+01s

DOI: 10.6041/j.issn.1000-1298.2025.10.048

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

48. Lightweight LIS-YOLO Model for Multi-target Fish Instance Segmentation

Accession number: 20254619496457

Title of translation: 多目标鱼类实例分割 LIS-YOLO 轻量化模型研究

Authors: Xu, Wenkai (1, 2); Lang, Ping (1, 2); Li, Daoliang (1, 2)

Author affiliation: (1) National Innovation Center for Digital Fishery, China Agricultural University, Beijing; 100083, China; (2) College of Information and Electrical Engineering, China Agricultural University, Beijing; 100083, China

Corresponding author: Li, Daoliang(dliangl@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: 10

Issue date: October 2025

Publication year: 2025

Pages: 94-101 and 139

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Accurate segmentation of underwater objects laid the foundation for studying aspects such as the behaviour and biomass of aquatic animals. However, existing underwater instance segmentation algorithms often lacked sufficient robustness when facing underwater environment-specific interferences, such as suspended particles, colour attenuation, and background noise, and enhancing the generalization ability of lightweight models in unstructured and dynamic underwater scenes remained a key challenge. To this end, a lightweight instance segmentation lightweight instance segmentation YOLO v8 (LIS- YOLO) model was proposed, which can effectively segment sturgeon, juvenile bass, and adult bass from different shooting angles, and a real-time underwater object segmentation system was developed based on PyQt5. Firstly, a lightweight and high-precision C2f Faster EMA module was designed, in which the original complex C2f module was replaced with a more lightweight C2f Faster module, and an efficient multi-scale attention mechanism was integrated to improve the feature extraction capability for small-object fish. Secondly, Wise IoU was introduced into the improved model to reduce harmful gradients caused by low-quality samples, thereby enhancing the model’ s segmentation capability in complex environments. Finally, a real-time multi-object instance segmentation system for underwater objects was developed by using a graphical user interface and the PyQt5 framework, enabling the visualization of different fish species. The experimental results showed that the LIS-YOLO model achieved precision, mean average precision, floating-point operations, and frame rate of 97. 2%, 95. 9%, 3. 60 × 1010 , and 127 f / s, respectively. The number of model parameters was compressed to 9. 0 × 106, accounting for 76. 3% of the original model. This research result not only provided an accurate and lightweight instance segmentation model for underwater object recognition but also explored the effectiveness of fish segmentation from different shooting angles, offering practical application value for improving the level of intelligent aquaculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Fish

Controlled terms: Image segmentation? - ?Learning systems? - ?Object recognition? - ?Underwater acoustics

Uncontrolled terms: Deep learning? - ?Instance segmentation? - ?Lightweight model? - ?Multi-object fish? - ?Multiobject? - ?Pyqt5? - ?Real- time? - ?Segmentation system? - ?Underwater objects? - ?YOLO v8

Classification code: 751.1 Acoustic Waves? - ?822.3 Food Products? - ?902.1 Engineering Graphics? - ?1101.2 Machine Learning? - ?1103.2 Computer Peripheral Equipment? - ?1106.3.1 Image Processing

Numerical data indexing: Percentage 2.00E+00%, Percentage 3.00E+00%, Percentage 9.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.009

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

49. Improved Model for Low Altitude Detection of Trees Infected by Pests and Diseases Using Agricultural Drones Based on DCA ? YOLO

Accession number: 20254619493679

Title of translation: 基于 DCA ? YOLO 的受病虫害侵染树木农业无人机低空检测模型

Authors: Xu, Xianghua (1); Zhou, Dejing (2); Yu, Chaoran (3); Xiong, Wanjie (2); Xiong, Yunshi (2); Wu, Baiying (2); Zheng, Xuanzhu (2); Ou, Deyuan (2)

Author affiliation: (1) Engineering Fundamental Teaching and Training Center, South China Agricultural University, Guangzhou; 510642, China; (2) College of Artificial Intelligence, South China Agricultural University, Guangzhou; 510642, China; (3) Vegetable Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou; 510640, China

Corresponding author: Xiong, Wanjie(xxwwjj@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: 10

Issue date: October 2025

Publication year: 2025

Pages: 479-491

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In view of the problems of low accuracy, precision and high computational cost when agricultural drones conduct multi-scale target detection and positioning of pest-infected trees in low-altitude environments, an improved target detection algorithm named DCA ? YOLO suitable for low-altitude operations was proposed. Firstly, the DCA dynamic channel attention mechanism was introduced. It worked together with the dual-branch collaborative attention to dynamically adjust the learning weights of features, enhancing the ability to perceive multi-scale features and context information of targets. A small target enhancement detection module was added at layer P2, and the DCA attention mechanism was added to the detection heads at layers P2 and P4, effectively optimizing the detection performance of small and medium-sized targets. The Inner ? IoU loss function was introduced to improve the fine recognition ability of easily confused targets in complex environmental backgrounds. To ensure the smooth operation of the model, the lightweight GhostNetv2 was used to optimize the backbone network, reducing the number of model parameters to 2. 34 × 106, with a remarkable lightweight effect. Finally, the improved BiFPN was used to optimize the neck network to further enhance the feature relearning ability. The experimental results showed that compared with the original model, DCA ? YOLO had 5. 2 percentage points increase in mAP0. 5 on the augmented validation set, 5. 6 percentage points increase in detection accuracy, 7. 1 percentage points increase in recall rate, and a reduction of 6. 7 × 105 in the number of parameters. The floating-point operations (FLOPs) were reduced by 26. 4%, with 16. 1% decrease in model weight size. Finally, it maintained an accuracy of 87. 6%, a recall rate of 87. 4%, and a mean average precision of 93. 1% . The dynamic channel attention (DCA) mechanism demonstrated a channel weighting contribution of 0. 41 ± 0. 07 and negative sample suppression capability of - 0. 12 ± 0. 03. It effectively optimized the defects of false positives and false negatives under the condition of model lightweighting. In conclusion, DCA ? YOLO significantly improved the model’s accuracy through the collaborative improvement of multiple modules, and can efficiently meet the application requirements of agricultural drones for real-time high-precision detection of pest-infected trees. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 36

Main heading: Target drones

Controlled terms: Aircraft detection? - ?C (programming language)? - ?Complex networks? - ?Digital arithmetic? - ?Drones? - ?Drops? - ?Forestry? - ?Iodine compounds? - ?Scales (weighing instruments)? - ?Signal detection ? - ?Trees (mathematics)

Uncontrolled terms: Attention mechanisms? - ?Detection using drone? - ?Disease and pest tree? - ?Dynamic channel attention? - ?Dynamic channel attention ?YOLO? - ?Dynamic channels? - ?Ghostnetv2? - ?Lightweight? - ?Low altitudes

Classification code: 301.1.1 Liquid Dynamics? - ?301.2.2 Electrohydrodynamics? - ?435.2 Tracking and Positioning? - ?652.1.2 Military Aircraft? - ?716.1 Information Theory and Signal Processing? - ?716.2 Radar Systems and Equipment? - ?804.1 Organic Compounds? - ?804.2 Inorganic Compounds? - ?821.1 Woodlands and Forestry? - ?942.1.7 Special Purpose Instruments? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1105 Computer Networks? - ?1106.1.1 Computer Programming Languages? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory

Numerical data indexing: Percentage 1.00E00%, Percentage 4.00E+00%, Percentage 6.00E+00%, Size 1.778E+04m

DOI: 10.6041/j.issn.1000-1298.2025.10.042

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

50. Modeling of Cotton Root Soil System Based on Discrete Element Method and Investigation of Interface Failure Mechanisms during Uprooting Process

Accession number: 20254619501312

Title of translation: 基于离散元的棉秆根系土壤系统建模与起拔过程界面破坏机制研究

Authors: Yasenjiang, Baikeli (1, 2); Song, Ling (1, 2); Yue, Yong (1, 2); Xu, Haodong (1, 2); Xing, Rensheng (1, 2)

Author affiliation: (1) Mechanical and Electronic Engineering Institute, Xinjiang Agricultural University, Urumqi; 830052, China; (2) Xinjiang Key Laboratory of Intelligent Agricultural Equipment, Urumqi; 830052, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 353-364

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In the current cotton stalk pulling process, the interaction mechanisms at the root-soil interface are highly complex, and there is a lack of high-fidelity simulation models, which significantly hinders the structural design and parameter optimization of pulling machinery. To address this issue, the discrete element method (DEM) was employed to construct a cohesive soil model and a root-soil composite system representative of typical cotton fields in Xinjiang, aiming to systematically reveal the interfacial mechanical responses and failure mechanisms during the pulling process. Soil bonding parameters were calibrated by using unconfined compression tests combined with a Box Behnken design (BBD) optimization approach, yielding optimal values: normal bonding stiffness of 1. 05 × 108 N / m3, tangential bonding stiffness of 3. 09 × 108 N / m3, normal bonding strength of 6. 95 × 105 Pa / m2, and tangential bonding strength of 6. 69 × 105 Pa / m2. The axial pressure simulation error was 1. 7%, confirming model accuracy. Using these parameters, a root-soil model was constructed in EDEM software and validated against field tests, with the predicted uprooting force (592. 56 N) closely matching the measured value (598. 2 N), showing a 1% error. Uprooting force-time curve analysis revealed four distinct response stages: elastic loading, peak loading, attenuation failure, and residual stabilization, reflecting shear stress buildup, bond rupture, root-soil slippage, and post-failure contact. The results showed that shear stress dominated resistance evolution, and the failure mode was jointly affected by root diameter and soil conditions. The research can provide an effective simulation framework for modeling root-soil systems for the design optimization of uprooting mechanisms. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 37

Main heading: Failure analysis

Controlled terms: Cotton? - ?Diffusion bonding? - ?Failure (mechanical)? - ?Machine design? - ?Shear flow? - ?Shear stress? - ?Software testing? - ?Soil testing? - ?Soils? - ?Stiffness ? - ?Stress analysis? - ?Structural design? - ?Structural optimization

Uncontrolled terms: Bonding model? - ?Cotton stalk? - ?Discrete elements method? - ?Failure mechanism? - ?Root soil interface? - ?Root uprooting simulation of cotton stalk? - ?Soil bonding model? - ?Soil interfaces? - ?Soil model? - ?Soil systems

Classification code: 201.8.1 Metal Bonding and Soldering? - ?214 Materials Science? - ?214.1 Mechanical Properties of Materials? - ?214.1.1 Stress and Strain? - ?301.1.5 Flow of Fluid-Like Materials? - ?408 Structural Design? - ?483.1 Soils and Soil Mechanics? - ?601 Mechanical Design? - ?821.5 Agricultural Products? - ?904 Design? - ?1106.9 Computer Software? - ?1201.7 Optimization Techniques? - ?1502.1.1.4.3 Soil Pollution Control

Numerical data indexing: Percentage 7.00E+00%, Pressure 6.90E+06Pa, Pressure 9.50E+06Pa, Surface tension 5.00E+08N/m, Surface tension 9.00E+08N/m, Force 2.00E+00N, Force 5.60E+01N, Percentage 1.00E00%

DOI: 10.6041/j.issn.1000-1298.2025.10.030

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

51. Research Progress of Audio Information Technology in Agricultural Field

Accession number: 20254619500878

Title of translation: 农业领域音频信息技术研究进展

Authors: Yu, Ligen (1, 2); Zhuang, Yanrong (1, 2); Qiu, Feng (1, 2); Ding, Xiaoli (1, 2); He, Jin (1, 2); Zhao, Yujie (1, 2); Yang, Gan (1, 2); Wu, Yue (1, 2); Zhao, Chunjiang (1, 2); Li, Qifeng (1, 2)

Author affiliation: (1) Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing; 100097, China; (2) National Engineering Research Center for Information Technology in Agriculture, Beijing; 100097, China

Corresponding authors: Zhao, Chunjiang(zhaocj@nercita.org.cn); Li, Qifeng(liqf@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: 10

Issue date: October 2025

Publication year: 2025

Pages: 223-248

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Audio technology, as a vital modality for agricultural information perception, offers significant advantages, including non-invasiveness, strong real-time responsiveness, and low implementation cost. These features enabled its broad application potential in the context of smart agriculture. A comprehensive review of recent advancements and representative applications of audio technology in both livestock farming and plants production was provided. In the domain of animal husbandry, it highlighted audio-based methods for recognizing feeding, estrus, drinking, and egg-laying behaviors, along with key techniques for emotion assessment, stress monitoring, disease detection, voiceprint identification, and sound source localization. In the plants sector, the acoustic emission mechanisms under abiotic stresses such as drought and freezing were covered, the regulatory effects of specific sound frequencies on plant physiology and gene expression were explored. Additionally, the current status of intelligent voice interaction systems in agriculture was outlined, including their roles in information services, machinery control, and voice-based consultation. Finally, it emphasized future research directions, advocating for the development of multimodal sensing integration, edge computing optimization, and standardized audio data platforms to advance the intelligent, efficient, and scalable application of audio technologies in modern agricultural systems. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 206

Main heading: Information services

Controlled terms: Agribusiness? - ?Agriculture? - ?Audio acoustics? - ?Audio systems? - ?Behavioral research? - ?Livestock? - ?Plants (botany)? - ?Smart agriculture

Uncontrolled terms: Agricultural fields? - ?Agricultural informations? - ?Audio information? - ?Audio information technology? - ?Audio technologies? - ?Information perception? - ?Livestock and poultry farming? - ?Plant production? - ?Smart agricultures? - ?Voice interaction

Classification code: 101.5 Ergonomics and Human Factors Engineering? - ?103 Biology? - ?751 Acoustics, Noise, Sound and Speech? - ?752 Sound Devices, Equipment and Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?903.4 Information Services? - ?971 Social Sciences

DOI: 10.6041/j.issn.1000-1298.2025.10.021

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

52. Sub-pixel Edge Detection of Measurement Image Based on Ruled Surface Model

Accession number: 20254619506376

Title of translation: 基于直纹曲面模型的测量图像亚像素边缘定位

Authors: Zhang, Jing (1); Zhao, Wenhui (1); Du, Po (2); Zhao, Wenzhen (1); Zhang, Ming (1)

Author affiliation: (1) School of Mechanical Engineering, Shenyang University of Technology, Shenyang; 110870, China; (2) Center of Engineering Training, Shenyang University of Technology, Shenyang; 110870, China

Corresponding author: Zhao, Wenhui(zhaowenhui@sut.edu.cn)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 802-808

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming at problems of existing measuring edges image detection models such as complex and difficult to solve，low detecting accuracy and slow speed，an edge detection algorithm was proposed based on the ruled surface model． In the algorithm，the sigmoid surface model was used instead of the traditional Gaussian integral surface model to represent the edges of the measured image． Combining the characteristics of the solution model，the gray value of the image was normalized in advance，and a logarithmic gray difference matrix was constructed． The light intensity compensation value was added to the transformed matrix elements to calibrate the errors caused by insufficient saturation or interference in the image． A visual measurement system and capture clear measurement images were built by using backlight illumination． The pixel equivalent Ar = 19． 472 1 μm / pixel and the light intensity compensation coefficient DLs = 0． 13 were determined by using dot matrix calibration plate and measuring block as objects． The experimental results of measuring block edge positioning error showed that compared with the Gaussian surface fitting algorithm，the edge detection algorithm based on the ruled surface model located sub-pixel edges smoother，with an edge straightness error of 0． 6 μm． Under the same software and hardware conditions，the computational efficiency was increased by 3． 3 times，the measurement accuracy was improved from 3． 3 μm to 1 μm． The algorithm proposed was applied to internal gear measurement of tooth profile total deviation． The gear images were collected，sub-pixel edge points of the outer arc and inner gear profile of the gear ring were located，the center of the inner gear from the outer arc edge points was determined，the initial phase angle of the tooth profile edge points was calculated，and the model based on the total deviation of the tooth profile was calculated． The calculation results of the total deviation of the tooth profile indicated that the error values were all lower than the maximum allowable value of the national standard． This visual edge localization algorithm had the potential to be applied to high-precision mechanical part measurement. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Edge detection

Controlled terms: Computational efficiency? - ?Error compensation? - ?Error detection? - ?Gear teeth? - ?Image enhancement? - ?Matrix algebra? - ?Pixels? - ?Signal detection? - ?Vision

Uncontrolled terms: Detection algorithm? - ?Edge point? - ?Light intensity? - ?Ruled surface model? - ?Ruled surfaces? - ?Sigmoids? - ?Sub-pixels? - ?Surface modeling? - ?Tooth profile? - ?Vision measurement

Classification code: 101.5 Ergonomics and Human Factors Engineering? - ?601.2 Machine Components? - ?716.1 Information Theory and Signal Processing? - ?731.1.1 Error Handling? - ?741.2 Vision? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1201.1 Algebra and Number Theory

Numerical data indexing: Size 1.00E-06m, Size 3.00E-06m to 1.00E-06m, Size 6.00E-06m

DOI: 10.6041/j.issn.1000-1298.2025.10.073

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

53. Simulation Analysis and Experiment of Vibration Fruit Removal Based on Mechanical Model of Camellia oleifera

Accession number: 20254619500736

Title of translation: 基于油茶枝果力学模型的振动脱果仿真与试验

Authors: Zhao, Enlong (1, 2); Zhao, Jinhui (1, 2); Zhuang, Tengfei (1, 2); Jin, Wenting (1, 2); Liu, Lijing (1, 2); Yuan, Yanwei (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: Yuan, Yanwei(yyw215@163.com)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 365-374

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In view of the current situation of Camellia oleifera fruit harvesting mainly relying on manual labor, low production efficiency and high labor cost, in order to solve the significant problems of Camellia oleifera fruit harvesting which seriously hinder the development of Camellia oleifera industry, explore a suitable vibrating picker for Camellia oleifera branches and trunks with suitable working parameters, and improve the picking efficiency and reduce labor intensity. The principle of forced vibration picking of the branches and trunks of the Camellia oleifera tree was analyzed, and the main factors affecting the forced vibration of the branches and trunks of the Camellia oleifera tree were obtained, including the working parameters of the excitation device (excitation position, excitation frequency, and excitation time) and the physical parameters of the Camellia oleifera tree (Young’s modulus, rotational moment of inertia of the branches and trunks, density, and cross-sectional area). The rigid-flexible coupling model of Camellia oleifera branch fruit excitation device was established and analyzed by one-factor simulation, and the level codes of field test factors were obtained. A three-factor, three-level field orthogonal test was designed to analyze the effects of the interaction of the factors on the test results. Under the combination of the optimal parameters of excitation position of 300 mm, excitation frequency of 13. 44 Hz, and excitation time of 7. 15 s, the shedding rate of Camellia oleifera fruits was 93. 86%, and that of buds was 22. 62%. Verification test showed that the relative error between Camellia oleifera fruit shedding rate and optimized value was 2. 62 percent point, the relative error between bud shedding rate and optimized value was 1. 46 percent point, the bud damage was less than 30% compared to that of manual picking, which was within the acceptable range, and effectively reduced labor intensity. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 27

Main heading: Flexible couplings

Controlled terms: Efficiency? - ?Elastic moduli? - ?Employment? - ?Forestry? - ?Fruits? - ?Plants (botany)? - ?Vibration analysis? - ?Wages

Uncontrolled terms: Branch dynamic model? - ?Camellia oleifera? - ?Camellia oleifera fruit picking? - ?Camellia oleifera fruits? - ?Dynamics models? - ?Fruit harvesting? - ?Labour intensity? - ?Simulation? - ?Trunk vibration type? - ?Working parameters

Classification code: 103 Biology? - ?214.1.3 Elasticity, Plasticity, Creep and Deformation? - ?601.2 Machine Components? - ?602.2 Mechanical Transmissions? - ?821.1 Woodlands and Forestry? - ?821.5 Agricultural Products? - ?901 Engineering Profession? - ?912.3 Personnel? - ?913.1 Production Engineering? - ?941.5 Mechanical Variables Measurements

Numerical data indexing: Frequency 4.40E+01Hz, Percentage 3.00E+01%, Percentage 4.60E+01%, Percentage 6.20E+01%, Percentage 8.60E+01%, Size 3.00E-01m, Time 1.50E+01s

DOI: 10.6041/j.issn.1000-1298.2025.10.031

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

54. Design and Experiment of Granular Fertilizer Collection Device for Deposition Distribution Testing of UAV-based Spreader

Accession number: 20254619501313

Title of translation: 面向无人飞机播撒作业沉积分布测试的颗粒肥料收集装置设计与试验

Authors: Zhou, Zhiyan (1, 2); Fan, Xiaolong (1, 3); Lin, Jianqin (1, 4); Deng, Konghong (1, 4); Liu, Zibo (1, 4); Jiang, Rui (1, 5)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) Guangdong Laboratory for Lingnan Modern Agriculture, 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) Key Laboratory of Key Technology on Agricultural Machine and Equipment, South China Agricultural University, Ministry of Education, Guangzhou; 510642, China

Corresponding author: Jiang, Rui(ruiojiang@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: 10

Issue date: October 2025

Publication year: 2025

Pages: 301-309

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The application of unmanned aerial vehicles (UAVs) for spreading operations is becoming increasingly widespread in agricultural production. The uniformity of the sediment distribution of granular fertilizers is an important indicator for measuring the performance of spreading operations. In sediment distribution testing, the completeness of granular fertilizer collection was critical to the accuracy of test results. To improve the collection rate of granular fertilizers during testing, optimization experiments on the material and structural parameters of the fertilizer collection device were conducted. A comparative test on the rebound height of different materials-polyvinyl chloride, rubber, foam mat, and Oxford fabric-was designed. The test results showed that Oxford fabric had the lowest rebound height, with an average rebound height of 2. 12 cm for urea and 2. 27 cm for compound fertilizer. A comparative test on the collection rates of granular fertilizers was also conducted by using conventional rigid materials (polyethylene plastic and cardboard) and four types of flexible materials. The results showed that flexible materials significantly improved the collection rates of both urea and compound fertilizer compared with rigid materials. Among them, Oxford fabric achieved the highest collection rates, with 96. 93% for urea and 99. 41% for compound fertilizer. Two types of collection devices-a conical and an arc-shaped design-were developed by using Oxford fabric. A bench test was designed to compare the collection rates of the different shapes. The results showed that the conical collection device outperformed the arc-shaped one, achieving collection rates of 96. 57% for urea and 98. 25% for compound fertilizer. Compared with the conventional rigid polyethylene plastic box, this represented an increase of approximately 45. 76 percentage points and 42. 68 percentage points, respectively. Field tests were conducted to measure the sediment distribution characteristics of granular fertilizers under UAV spreading operations. The results showed that under three application levels of 75 kg/ hm2, 150 kg/ hm2, 225 kg/ hm2, the amount of urea collected by the conical device made of flexible material was 0. 75 g, 1. 41 g, and 2. 23 g more than that collected by the rigid plastic box, respectively. Under the same application levels, the amount of compound fertilizer collected was increased by 1. 13 g, 2. 37 g, and 3. 67 g, respectively. The conical collection device made of Oxford fabric effectively reduced secondary rebounds of granular fertilizers and significantly improved the accuracy of sediment distribution testing. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Unmanned aerial vehicles (UAV)

Controlled terms: Antennas? - ?Deposition? - ?Plastic bottles? - ?Polyvinyl chlorides? - ?Rubber? - ?Urea fertilizers

Uncontrolled terms: Aerial vehicle? - ?Collection device? - ?Collection rates? - ?Compound fertilizer? - ?Deposition distribution? - ?Flexible materials? - ?Granular fertilizers? - ?Sediment distribution? - ?Spreading? - ?Unmanned aerial vehicle

Classification code: 205.1.1 Organic Polymers? - ?207.1 Polymer Products? - ?212.1 Natural Rubber? - ?652.1 Aircraft? - ?694.1 Packaging Materials and Equipment? - ?716.5.1 Antennas? - ?802.3 Chemical Operations? - ?821.3 Agricultural Chemicals

Numerical data indexing: Mass 4.10E-02kg, Mass 6.70E-02kg, Mass 7.50E+01kg, Mass 7.50E-02kg, Percentage 2.50E+01%, Percentage 4.10E+01%, Percentage 5.70E+01%, Percentage 9.30E+01%, Size 1.20E-01m, Size 2.70E-01m, Mass 1.30E-02kg, Mass 1.50E+02kg, Mass 2.25E+02kg, Mass 2.30E-02kg, Mass 3.70E-02kg

DOI: 10.6041/j.issn.1000-1298.2025.10.025

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

55. Ripe Fruit Detection Model of Passion Fruit Based on Improved YOLO v10n

Accession number: 20254619496422

Title of translation: 基于改进 YOLO v10n 的百香果成熟果实检测方法

Authors: Zhu, Shiping (1); Zhang, Yue (1); Tang, Maojie (1); Zou, Jiaqi (1); Liu, Yinfeng (1)

Author affiliation: (1) College of Engineering and Technology, Southwest University, Chongqing; 400716, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 549-557 and 595

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Identifying and locating mature passion fruits in a natural environment is the primary task for achieving their automated picking． However，in natural environments，the picking of ripe passion fruits faces significant challenges such as changes in light，tree branch obstructions，and overlapping fruits，which require an efficient visual system as a support to achieve automated picking of ripe passion fruits．To this end，a passion fruit mature fruit detection model ＲC-YOLO v10n was proposed based on the improved YOLO v10n． The passion fruit images were collected in a natural environment and the passion fruit fruits in the images were labeled as unripe，nearly ripe，and ripe fruits． Based on the single-stage detection model YOLO v10n，the ＲFAConv module was introduced at its Neck end to replace the C2f module of the 9th layer and the C2fCIB module of the 12th layer，in order to enhance the feature expression ability and feature fusion effect of the model． And the CAＲAFE upsampling operator was used to replace the linear interpolation upsampling module in the original model to enrich the semantic information of upsampling． The test results showed that，compared with the original model，the precision of the model was increased by 3 percentage points，the recall rate was increased by 1． 6 percentage points，the mean average precision was increased by 4． 6 percentage points，the memory usage of the model was 13． 72 MB，and the detection time of a single image was 0． 22 s． The performance of the model was superior to that of Faster Ｒ-CNN and the YOLO series of object detection algorithms． The models before and after the improvement were deployed on Jetson Nano for testing． The results showed that the detection effect of the improved model was significantly improved compared with the original model． The detection time of a single image was 1． 78 s． The improved model had good application value． Based on the actual harvesting situation of passion fruits in the orchard，two picking strategies were provided: the first one was to pick near-ripe and ripe passion fruits，with a picking accuracy rate of 89． 25%．Secondly，only ripe passion fruits were picked，with a picking accuracy rate of 92． 08%． The research result can provide a reference for the research on the identification and automated picking of mature passion fruits. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Fruits

Controlled terms: Ability testing? - ?Automation? - ?Fake detection? - ?Feature extraction? - ?Forestry? - ?Image enhancement? - ?Interpolation? - ?Object detection? - ?Object recognition? - ?Orchards ? - ?Signal detection

Uncontrolled terms: CAＲAFE? - ?Detection models? - ?Natural environments? - ?Original model? - ?Passion fruits? - ?Percentage points? - ?Ripe fruit? - ?Upsampling? - ?YOLO v10n? - ?ＲFAConv

Classification code: 716.1 Information Theory and Signal Processing? - ?731 Automatic Control Principles and Applications? - ?821.1 Woodlands and Forestry? - ?821.4 Agricultural Methods? - ?821.5 Agricultural Products? - ?903.1 Information Sources and Analysis? - ?912.3 Personnel? - ?1101.2 Machine Learning? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1201.9 Numerical Methods

Numerical data indexing: Percentage 2.50E+01%, Percentage 8.00E+00%, Time 2.20E+01s, Time 7.80E+01s

DOI: 10.6041/j.issn.1000-1298.2025.10.049

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

56. Design and Experiment of Rail-type Harvesting System for Penaeus vannamei in Greenhouse Ponds

Accession number: 20254519450430

Title of translation: 轨道式南美白对虾捕捞系统设计与试验

Authors: Li, Jun (1); Zhao, Qiang (1); Fan, Zhangchen (1); Wu, Gang (2); Ma, Tianli (1, 3); Chen, Leilei (1); Hu, Qingsong (1)

Author affiliation: (1) College of Engineering, Shanghai Ocean University, Shanghai; 201306, China; (2) Changzhou Huaxi Fishery Co., Ltd., Changzhou; 213300, China; (3) Shanghai Chongming Farm Co., Ltd., Bright Food Croup, Shanghai; 202174, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 54-62

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In response to the persistent challenges of high manual intervention, intensive labor requirements, low operational efficiency, and a critical lack of automated equipment suitable for facility-based greenhouse ponds during the harvesting of Penaeus vannamei, a novel rail-based harvesting system was proposed based on an integrated modular concept. Considering the specific structural constraints of greenhouse facilities and practical operational requirements, the system’s fundamental design specifications and functional performance indicators were systematically defined, covering all key processes including trap deployment, catching, and transportation. Employing an advanced modular design approach, a comprehensively integrated system was developed, incorporating a centralized power supply system, a precision lifting mechanism, and an efficient catching device. All key parameters, including the drive motor, lifting motor, and optimized track radius, were meticulously calculated and appropriately selected through rigorous engineering analysis. Finite element analysis via ANSYS software was comprehensively conducted to simulate and evaluate the structural strength of the frame and the adsorption performance of the electromagnet, thoroughly verifying structural safety and functional feasibility. A full prototype of the rail-based harvesting system was manufactured and subjected to extensive operational tests evaluating travel stability, lifting reliability, and harvesting efficiency, conclusively confirming the effectiveness of the overall design. The final results demonstrated an average capture success rate of 81.25%, a total operation time of 240 s per cycle, and a consistent average operating speed of 0.75 m/s, collectively indicating high operational efficiency, remarkable stability, and reliable performance. The research result can provide valuable insights and practical reference for the design and broader application of automated harvesting equipment in modern intensive aquaculture environments. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Efficiency

Controlled terms: Agricultural machinery? - ?Greenhouses? - ?Harvesting? - ?Software prototyping

Uncontrolled terms: Electromagnetic adsorption? - ?Electromagnetics? - ?Facility-based greenhouse pond? - ?Finite element analyse? - ?Harvesting system? - ?Labour requirements? - ?Manual intervention? - ?Operational efficiencies? - ?Penaeus vannamei? - ?Rail-based harvesting

Classification code: 821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?821.7 Farm Buildings and Other Structures? - ?913.1 Production Engineering? - ?1106.9 Computer Software

Numerical data indexing: Percentage 8.125E+01%, Time 2.40E+02s, Velocity 7.50E-01m/s

DOI: 10.6041/j.issn.1000-1298.2025.10.005

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

57. Binocular Stereo Vision-based Measurement Method for Body Size and Body Weight Measurement of Frozen Yellowfin Tuna

Accession number: 20254519450409

Title of translation: 基于双目立体视觉的冷冻黄鳍金枪鱼体尺与体质量测量方法

Authors: Liu, Yuqing (1); Zhang, Xincheng (1); Wan, Jiacheng (1); Wang, Chenye (1); Sui, Chenxi (1); Sui, Hengshou (2); Wan, Rong (3)

Author affiliation: (1) College of Engineering Science and Technology, Shanghai Ocean University, Shanghai; 201306, China; (2) CNFC Overseas Fishery Co., Ltd., Beijing; 100032, China; (3) College of Marine Biological Resources and Management, Shanghai Ocean University, Shanghai; 201306, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 36-44

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the issues of significant measurement errors, low operational efficiency, and safety hazards inherent in the traditional manual weighing and measurement process for frozen yellowfin tuna (Thunnus albacares), a fish body dimension and body weight estimation method was proposed based on binocular stereo vision and the segment anything model (SAM) image segmentation algorithm. Using frozen yellowfin tuna in real processing environments as the research subject, the method comprised four modules; fish body target detection, fish body image segmentation, fish body dimension extraction, and fish body weight estimation modeling. The method involved capturing images of yellowfin tuna by using a depth camera. To tackle the challenge of complex environments in aquatic product processing settings, the YOLO v8n algorithm was employed to detect frozen yellowfin tuna, coupled with the BotSort object tracking algorithm and a dynamic mask algorithm to eliminate interference from extraneous objects. The SAM image segmentation algorithm was then used to precisely extract the image and depth information of the frozen yellowfin tuna. Combined with the Open3D framework, a 3D point cloud map and dimensional information of the fish were generated. Based on the extracted dimensions and a body weight estimation model, the method achieved accurate body weight prediction for frozen yellowfin tuna. The measured body dimensions and body weight data exhibited small errors; the mean absolute percentage error ( MAPE) for body length was 4.35%, for body height was 3.37%, the mean absolute error (MAE) for estimated body weight was 0.604 kg, and the mean absolute percentage error was 4.63%. This method can efficiently and accurately achieve 3D measurement and body weight estimation of yellowfin tuna, providing a reference for the automated management of frozen yellowfin tuna processing. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 31

Main heading: Image segmentation

Controlled terms: 3D modeling? - ?Anthropometry? - ?Binocular vision? - ?Binoculars? - ?Computer vision? - ?Fish? - ?Measurement errors? - ?Object detection? - ?Stereo image processing? - ?Stereo vision ? - ?Three dimensional computer graphics? - ?Weighing

Uncontrolled terms: Body dimensions? - ?Body size measurement? - ?Body sizes? - ?Body weight? - ?Body weight measurement? - ?Images segmentations? - ?Size measurements? - ?Three-dimensional point clouds? - ?Weights estimation? - ?Yellowfin thunnu albacare

Classification code: 101.4 Biomechanics, Bionics and Biomimetics? - ?731.1.1 Error Handling? - ?731.6 Robot Applications? - ?741.2 Vision? - ?741.3 Optical Devices and Systems? - ?822.3 Food Products? - ?902.1 Engineering Graphics? - ?942.1.7 Special Purpose Instruments? - ?1106.2 Data Handling and Data Processing? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision? - ?1201.12 Modeling and Simulation

Numerical data indexing: Mass 6.04E-01kg, Percentage 3.37E+00%, Percentage 4.35E+00%, Percentage 4.63E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.003

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

58. Fish Abnormal Behavior Detection and Classification via Multi-feature Fusion and Multi-tiered Hypergraph Network

Accession number: 20254519450410

Title of translation: 融合多特征与分层超图网络的鱼类异常行为检测与分类方法

Authors: Long, Wei (1, 2); Sun, Cuisuo (1); Zhang, Chen (1); Jiang, Linhua (1, 2); Hu, Lingxi (1, 3); Xu, Lihong (4)

Author affiliation: (1) School of Information Engineering, Huzhou University, Huzhou; 313000, China; (2) Zhejiang-French Digital Monitoring Laboratory for Aquatic Resources and Environment, Huzhou; 313000, China; (3) Huzhou Key Laboratory of Waters Robotics Technology, Huzhou University, Huzhou; 313000, China; (4) College of Electronics and Information Engineeriing, Tongji University, Shanghai; 201804, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 102-109 and 118

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Real-time monitoring of abnormal fish behaviors plays a crucial role in enhancing management efficiency, reducing disease risk, and optimizing feed strategies in modern intelligent aquaculture. To address challenges such as image blurring, complex backgrounds, behavioral diversity, and the trade-off between detection accuracy and computational efficiency, a robust detection model, YOLO 11-AB, was presented based on an improved YOLO 11 architecture. The model incorporated a multi-scale convolution module ( C3k2-PKI Module) in the backbone network to enhance perception of behavioral features at various scales. A lightweight mixed local channel attention (MLCA) mechanism was integrated into the feature extraction stage to effectively fuse channel and spatial information, thereby improving feature representation. Additionally, the neck of the model adopted a hypergraph-based cross-level representation network (HyperC2Net) and a mixed aggregation network (MANet), further strengthening the detection and discrimination of abnormal behaviors in complex underwater environments. Experimental results demonstrated that the proposed model achieved significant improvements in detection accuracy, computational efficiency, and classification performance, with a 3.3 percentage points increase in precision, a 5.9 percentage points increase in recall, and a 4.4 percentage points increase in mean average precision compared with traditional methods. This approach provided technical support for early warning and management of fish diseases in high-density factory aquaculture, offering practical value for enhancing efficiency and promoting the sustainable development of the aquaculture industry. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 32

Main heading: Economic and social effects

Controlled terms: Aquaculture? - ?Complex networks? - ?Computational efficiency? - ?Fish

Uncontrolled terms: Abnormal behavior detections? - ?Attention mechanisms? - ?Detection accuracy? - ?Features fusions? - ?Hyper graph? - ?Multi-scale feature fusion? - ?Multi-scale features? - ?Percentage points? - ?Smart aquaculture? - ?YOLO 11-AB model

Classification code: 821.4 Agricultural Methods? - ?822.3 Food Products? - ?971 Social Sciences? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1105 Computer Networks

DOI: 10.6041/j.issn.1000-1298.2025.10.010

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

59. Giant Salamander Detection Model Based on Improved YOLO 11n-EWL

Accession number: 20254519449362

Title of translation: 基于改进YOLO 11n-EWL的大鲵检测模型研究

Authors: Tian, Fang田芳 (1); Yang, Xinyao (1); Sun, Nanqing (1); Gao, Hang (1); Chen, Junyi (1); Qin, Zhangnian (2); Tuo, Xingmin (2); Chen, Lirong (2); He, Lei (2)

Author affiliation: (1) College of Intelligent Systems Seienee and Engineering, Hubei Minzu University, Enshi; 445600, China; (2) Hubei Xianfeng Zhongjianhe Giant Salamander National Nature Reserve Management Center, Enshi; 445600, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 110-118

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to achieve rapid and accurate identification of giant salamanders in complex outdoor environments, an improved recognition model was proposed based on YOLO 11n. The model incorporated an efficient multi-scale attention module ( EMA) in the backbone layer, replaced the complete intersection over union (CIoU) loss function with the Wise-IoU (WIoU) loss function, and introduced lightweight adaptive extraction of convolutions (LAE) in the head layer. Through ablation experiments and comparative tests, it was found that the improved model achieved 94.85% recall rate, 95.39% precision rate, 95.12% F1 score, and 77.20 f/s frame rate, with a model memory footprint of 11.56 MB, and the floating-point operation count was 8.65 × 109. Compared with the baseline YOLO 11n, the improved model outperformed it by 5.70 percentage points, 6.13 percentage points, 5.92 percentage points, and 27.1 f/s in terms of recall rate, precision rate, F1 score, and frame rate, respectively. The proposed model YOLO 11n-EWL demonstrated significant improvements in model stability, recognition speed, and accuracy. The improved model can meet the real-time detection requirements of giant salamanders in the wild and can adapt to outdoor work in the long term, and can construct a set of giant salamander all-weather image intelligent recognition and behavior detection system. The research result can provide theoretical and technical support for the real-time detection of giant salamanders in complex outdoor environments. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Deep learning

Controlled terms: Digital arithmetic? - ?Image enhancement? - ?Object detection? - ?Object recognition? - ?Signal detection

Uncontrolled terms: Andria davidianu? - ?Deep learning? - ?Detection device? - ?Giant salamander detection device? - ?Loss functions? - ?Objects detection? - ?Outdoor environment? - ?Percentage points? - ?Recall rate? - ?YOLO 11

Classification code: 716.1 Information Theory and Signal Processing? - ?1101.2.1 Deep Learning? - ?1102.1 Computer Theory, Includes Computational Logic, Automata Theory, Switching Theory, Programming Theory? - ?1106.3.1 Image Processing? - ?1106.8 Computer Vision

Numerical data indexing: Percentage 9.485E+01%, Percentage 9.512E+01%, Percentage 9.539E+01%

DOI: 10.6041/j.issn.1000-1298.2025.10.011

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

60. Complete Coverage Path Planning Strategy for Offshore Fishing Multi-USV Based on Improved QMIX Algorithm

Accession number: 20254519450405

Title of translation: 基于改进QMIX算法的远洋捕捞多无人艇全覆盖路径规划策略研究

Authors: Wu, Qingyun (1); Wang, Dong (1); Tao, Jun (1); Li, Zhijian (1); Yin, Yijie (1)

Author affiliation: (1) College of Engineering, Shanghai Ocean University, Shanghai; 201306, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 63-70

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In offshore fishing missions, multiple unmanned surface vessels (USVs) need to perform comprehensive coverage path planning within specific waters to detect fish distribution. However, traditional multi-agent reinforcement learning lacks the capability to simultaneously consider both its own and neighboring agents’ states, coupled with an unclear feedback mechanism, leading to low efficiency and high redundancy in coverage tasks. To address these issues, a comprehensive coverage path planning strategy for offshore fishing was proposed by using multiple USVs based on the improved QMIX algorithm (LH-QMIX). QMIX was a multi-agent reinforcement learning method, consisting of a mixing network and multiple agent networks, which integrated the local Q-values from each agent network into a global Q-value through the mixing network to guide agent actions. Considering that communication and perception ranges were typically limited in offshore environment, a local loss function was introduced for each agent network to provide a clearer feedback mechanism. Additionally, a hybrid attention mechanism was incorporated to enhance collaboration among USVs. The proposed LH-QMIX algorithm was compared with the independent Q-learning (IQL) algorithm and the original QMIX algorithm through simulations in both simple and complex obstacle environment. Simulation result showed that compared with the traditional QMIX algorithm, the LH - QMIX achieved improvements of 6.9% and 10.6% in coverage efficiency in simple and complex obstacle environments, respectively, with more stable reward curves after convergence. The research provided an effective solution for multiple USVs to efficiently achieve comprehensive coverage in offshore fishing missions, thereby enhancing the efficiency of offshore fishing operations. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Efficiency

Controlled terms: Complex networks? - ?Feedback control? - ?Fish detectors? - ?Fisheries? - ?Fishing (oil wells)? - ?Fishing vessels? - ?Intelligent agents? - ?Learning algorithms? - ?Mixer circuits? - ?Mixing ? - ?Motion planning? - ?Multi agent systems? - ?Offshore oil well production? - ?Reinforcement learning

Uncontrolled terms: Agent network? - ?Coverage efficiencies? - ?Coverage path planning? - ?LH-QMIX algorithm? - ?Model stability? - ?Multi-agent reinforcement learning? - ?Multi-unmanned surface vessel path planning? - ?Offshores? - ?Planning strategies? - ?Unmanned surface vessels

Classification code: 471.2 Oceanographic Research Instruments? - ?471.5 Sea as Source of Minerals and Food? - ?511.1 Oil Field Production Operations? - ?512.1.2 Petroleum Development Operations? - ?672.2 Noncombat Naval Vessels? - ?713.3 Modulators, Demodulators, Limiters, Discriminators, Mixers? - ?731 Automatic Control Principles and Applications? - ?802.3 Chemical Operations? - ?822 Food Technology? - ?913.1 Production Engineering? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1105 Computer Networks? - ?1106.9 Computer Software

Numerical data indexing: Percentage 1.06E+01%, Percentage 6.90E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.006

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

61. Bayesian Optimization-based Multilayer Perceptron Prediction Algorithm for Catfish Feeding Quantity

Accession number: 20254519449342

Title of translation: 基于贝叶斯优化的鮰鱼投饲量多层感知机预测算法研究

Authors: Zhu, Yejun (1); Xu, Weixiong (1); Sun, Zhicheng (1); Li, Dongfang (1); Lü, Tieli (1); Li, Hongran (2); Xiao, Maohua (1)

Author affiliation: (1) College of Engineering, Nanjing Agricultural University, Nanjing; 210095, China; (2) School of Computer Engineering, Jiangsu Ocean University, Lianyungang; 222005, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 165-174

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: At present, the eost of aquaculture is continuously rising, with feed costs accounting for a significant proportion. Most feeding methods rely on empirical judgment and lack long-term tracking and prediction capabilities, which can easily lead to feed waste, water pollution, and negative impacts on the growth of aquatic animals. To address the lack of long-term prediction ability, the main factors affecting catfish feeding were first analyzed, water temperature, dissolved oxygen content, ammonia nitrogen content, average weight of fish, the total number, and meteorological conditions were selected as the input variables for the feeding calculation model, a multilayer perceptron (MLP) feeding calculation model was established, the data was normalized, and a model training dataset was constructed. After the model was established, a Bayesian optimization algorithm was used to optimize the hyperparameters of the MLP model to enhance its performance. Finally, a detailed comparative analysis between the actual feeding quantity and the model-predicted feeding quantity, using data from the three breeding ponds in the test set, verified the model’s performance and generalization ability. The average absolute percentage error remained below 4%, and the absolute error was controlled at less than 700 kg during August and September, in the peak feeding period for the 11# ponds. However, during special periods such as fish disease, harvesting, and fishing in the breeding ponds, breeders would adjust the feeding amount accordingly, leading to a large error between the model-predicted feeding amount and the actual feeding amount. This also highlighted the model’s limitations in coping with extraordinary events. Meanwhile, the traditional MLP model, random forest model, support vector machine model, and BO-MLP model were selected for comparative tests to verify the superior performance of the described model. Overall, the research results presented were of great significance as an important decision support tool for the aquaculture industry, improving feed utilization and enhancing aquaculture efficiency. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Decision support systems

Controlled terms: Ammonia? - ?Barium compounds? - ?Bayesian networks? - ?Feeding? - ?Fish? - ?Fish ponds? - ?Fisheries? - ?Forecasting? - ?Lakes? - ?Optimization ? - ?Statistical tests? - ?Support vector machines? - ?Water pollution

Uncontrolled terms: Bayesian optimization? - ?Breeding ponds? - ?Calculation models? - ?Feeding amount? - ?Feeding quantity? - ?Long-term prediction? - ?Multilayers perceptrons? - ?Perceptron predictions? - ?Performance? - ?Prediction algorithms

Classification code: 407 Maritime and Port Structures; Rivers and Other Waterways? - ?444.1 Surface Water? - ?471.5 Sea as Source of Minerals and Food? - ?691.2 Materials Handling Methods? - ?804.2 Inorganic Compounds? - ?821.4 Agricultural Methods? - ?822 Food Technology? - ?822.3 Food Products? - ?912.2 Management? - ?1101.2 Machine Learning? - ?1106 Computer Software, Data Handling and Applications? - ?1201.5 Computational Mathematics? - ?1201.7 Optimization Techniques? - ?1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory? - ?1202 Statistical Methods? - ?1202.2 Mathematical Statistics? - ?1502.1.1.2 Water Pollution

Numerical data indexing: Mass 7.00E+02kg, Percentage 4.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.017

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

62. Hybrid A * and Optimal Control-based Trajectory Planning Method for Precise Field Turning of Agricultural Machines

Accession number: 20254519444933

Title of translation: 基于 Hybrid A* 与最优控制的农机精准进田转场轨迹规划方法

Authors: Chi, Ruijuan (1); Fu, Guohui (1); Ma, Yueqi (1); Ban, Chao (1); Su, Tong (1); Chen, Jiayi (1); Li, Zhimin (1)

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

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 321-331

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In narrow farm roads, the planning of farm machinery movement trajectories affects the smoothness of the driving trajectory, as well as the quality and efficiency of field operations. Aiming to address the current issues in farm machinery path planning—such as large trajectory curvature, insufficient smoothness, and imprecise speed planning at narrow turns, which hinder optimal tracking performance, a method for precise farm machinery movement trajectory planning was proposed based on Hybrid A * and optimal control. By obtaining the prior grid map of the field entry, the initial pose, and the target pose, the Hybrid A * algorithm was used to obtain the optimal or near-optimal field entry path that satisfied the agricultural machinery’s kinematic constraints. Time information was assigned to path nodes, and initial solutions required for solving nonlinear problems were obtained through data preprocessing. Using the optimal control problem method, a multi-objective cost function was established under multiple constraints, including agricultural machinery kinematic constraints, two-point boundary value constraints, dynamic constraints, and obstacle avoidance constraints, to achieve the objectives of shortening transfer time, improve agricultural machinery maneuverability, and enhance trajectory smoothness; the optimal control problem was converted into a nonlinear programming (NLP) problem and solved by using a nonlinear solver to obtain the agricultural machinery field entry and exit trajectory and velocity sequence. Using the Yanmar VP6E rice transplanter as the experimental platform, simulations and real-vehicle experiments were conducted in two scenarios; the agricultural machinery head facing away from the field access channel and the head facing toward the field access channel. Experimental results showed that the average trajectory curvature was 0. 231 2 ~ 0. 251 7 m “, indicating good trajectory smoothness consistent with vehicle kinematic characteristics. In the trajectory tracking real-vehicle experiments, the average absolute lateral deviation was from 1. 56 cm to 2. 59 cm, the average absolute heading angle deviation was 0. 97° to 1. 54°, the maximum absolute speed deviation was 0. 058 m/s to 0. 102 m/s, and the average absolute speed was 0.454 m/s to 0.528 m/s. Therefore, the rice transplanting machine effectively tracked the trajectory generated by the trajectory planning method, enabling precise and rapid field entry and exit on narrow farm roads. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 25

Main heading: Nonlinear programming

Controlled terms: Agricultural machinery? - ?Agriculture? - ?Costs? - ?Kinematics? - ?Maneuverability? - ?Nonlinear simulations? - ?Optimal control systems? - ?Simulation platform? - ?Trajectories

Uncontrolled terms: Farm machinery? - ?Farm roads? - ?Hybrid A*? - ?Kinematic constraints? - ?Movement trajectories? - ?Optimal controls? - ?Precision transit? - ?Trajectory Planning? - ?Trajectory planning method? - ?Trajectory-tracking

Classification code: 656 Space Flight and Research? - ?731.1 Control Systems? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?911 Cost and Value Engineering; Industrial Economics? - ?1106.5 Computer Applications? - ?1201.7 Optimization Techniques? - ?1201.12 Modeling and Simulation? - ?1301.1.1 Mechanics

Numerical data indexing: Size 5.60E-01m to 2.00E-02m, Size 5.90E-01m, Size 7.00E+00m, Velocity 1.02E+02m/s, Velocity 4.54E-01m/s to 5.28E-01m/s, Velocity 5.80E+01m/s to 0.00E00m/s

DOI: 10.6041/j.issn.1000-1298.2025.10.027

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

63. Design and Testing of Bivariate Regulation System for Variable Diameter Centrifugal Fertilizer Spreaders in Oilseed Rape

Accession number: 20254519444928

Title of translation: 油菜变径离心式排肥器双变量调控系统设计与试验

Authors: Ding, Youchun (1, 2); Zhang, Dongjin (1, 2); Dong, Wanjing (1, 2); Xu, Chunbao (1, 2); Li, Haopeng (1, 2)

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 340-352

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: A variable-rate fertilization control system was developed for high-speed rapeseed seeding operations, based on a dual-variable control sequence involving the rotational speed and opening of a variable-radius centrifugal centralized fertilizer applicator. A GA — MOPSO fertilization decision model was constructed by analyzing the mechanisms of the multi-objective particle swarm optimization (MOPSO) and genetic algorithm (GA), and formulating a bi-objective model with fertilization error and controller response time based on calibration experiments. An integral sliding mode control (ISMC) algorithm was designed for precise control of the fertilizer shaft speed. Simulation results showed that at varying target fertilization rates, the GA — MOPSO algorithm achieved hypervolume indicators of 1. 004, 1. 029, and 1. 023 across 30 independent runs, with superior convergence and uniformity compared with that of MOPSO and DE -MOPSO. The ISMC algorithm achieved a steady-state time of 0. 212 s, a steady-state error of 0.013%, and zero overshoot, outperforming both PID and fuzzy PID controllers. Bench tests indicated that, with a weight vector of (0. 9, 0. 1), the proposed decision model reduced the mean relative fertilization error from 4. 17% to 2. 27% and the average response time from 0. 92 s to 0. 83 s. The ISMC algorithm achieved an average error of 2. 73%, with row-to-row coefficient of variation under 5. 62%, outperforming traditional controllers. Road tests showed a mean relative error of 3. 56% and an average response time of 0. 79 s. Field trials demonstrated that at speed of 6 ~ 12 km/h and application rates of 300 -600 kg/hm, the error remained below 4. 90% and the maximum response time was 1. 08 s. The research result can provide technical support for high-speed variable-rate fertilization with centralized fertilizer applicators. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 29

Main heading: Sliding mode control

Controlled terms: Agricultural machinery? - ?Controllers? - ?Errors? - ?Fertilizers? - ?Genetic algorithms? - ?Oilseeds? - ?Particle swarm optimization (PSO)? - ?Proportional control systems? - ?Response time (computer systems)? - ?Three term control systems

Uncontrolled terms: Dual-variable fertilization? - ?Fertilisation? - ?Integral sliding mode control? - ?Multi objective particle swarm optimization? - ?Oil seed rape? - ?Sliding mode control algorithms? - ?Variable diameter? - ?Variable diameter centrifugal? - ?Variable fertilizations? - ?Variable rate fertilization

Classification code: 731.1 Control Systems? - ?731.1.1 Error Handling? - ?732.1 Control Equipment? - ?821.2 Agricultural Machinery and Equipment? - ?821.3 Agricultural Chemicals? - ?821.5 Agricultural Products? - ?1106 Computer Software, Data Handling and Applications? - ?1201.7 Optimization Techniques? - ?1502.1.1.3 Soil Pollution

Numerical data indexing: Mass 3.00E+02kg to 6.00E+02kg, Percentage 1.30E-02%, Percentage 1.70E+01% to 2.00E+00%, Percentage 2.70E+01%, Percentage 5.60E+01%, Percentage 6.20E+01%, Percentage 7.30E+01%, Percentage 9.00E+01%, Size 6.00E+03m to 1.20E+04m, Time 2.12E+02s, Time 7.90E+01s, Time 8.00E+00s, Time 8.30E+01s, Time 9.20E+01s to 0.00E00s

DOI: 10.6041/j.issn.1000-1298.2025.10.029

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

64. Development Trends of Agricultural Engineering Technology Based on Bibliometrics

Accession number: 20254519444922

Title of translation: 基于文献计量学的农业工程技术发展动态分析

Authors: Fu, Longsheng (1, 2); Jia, Bo (1); Liu, Xiaojuan (1); He, Leilei (1); Yang, Liling (3); Mao, Wulan (1, 3); Li, Rui (1)

Author affiliation: (1) College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling; 712100, China; (2) Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Shaanxi, Yangling; 712100, China; (3) Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi; 830000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 249-276

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to gain a macro understanding of development dynamics, frontiers, and research hotspots in agricultural engineering technology, and better promote progress in the field, a bibliometric analysis of related articles from January 2015 to June 2025 was conducted, using Web of Science Core Collection and CNKI databases. The main conclusions were as follows; agricultural engineering technology exhibited explosive growth, with annual publications in international journals rising from 64 in 2015 to 910 in 2024. Compared with the Chinese journals increase from 153 publications in 2015 to 288 in 2021, the international growth rate was significantly faster, with an average annual growth rate exceeding 30%, highlighting the vitality of research driven by frontier technologies, including artificial intelligence, robotics, and remote sensing. Collaboration among countries, particularly China, United States, and Brazil, was relatively close, with China and United States serving as the main drivers of agricultural engineering technology, jointly accounting for 75.36% of the total publications. However, international cross-team collaboration remained somewhat limited. Computers and Electronics in Agriculture (48. 18%) is ranking the first in the international journals, Transactions of the Chinese Society of Agricultural Engineering (44.61%) and Transactions of the Chinese Society for Agricultural Machinery (32. 69%) are the first two Chinese journals, leading among peer journals. Research themes in agricultural engineering gradually evolved from early-stage simple classification tasks to intelligent technologies, such as artificial intelligence, deep learning, and machine learning. In response to the complexity and interdisciplinary demands of agricultural engineering, future efforts should prioritize strengthening the algorithm layer by integrating artificial intelligence and big data technologies to enhance multi-source data modeling and data-driven decision-making, thereby enabling intelligent decision-making in agricultural systems. The perception layer should achieve precise acquisition of environmental and crop conditions through multimodal sensing and 3D reconstruction. The execution layer can leverage adaptive, low-cost smart agricultural machinery or robots to perform efficient operations, while the support layer should ensure system efficiency through edge computing, cloud services, and digital twins. By integrating key technologies such as multimodal sensing, 3D reconstruction, and digital twins, a closed-loop “perception - modeling - decision - execution” system can be established, providing strong support for the intelligent and sustainable development of agriculture. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 169

Main heading: Agricultural engineering

Controlled terms: Agricultural robots? - ?Agricultural technology? - ?Big data? - ?Deep learning? - ?Edge computing? - ?Environmental technology? - ?Growth rate? - ?Intelligent systems? - ?Learning systems? - ?Publishing ? - ?Smart agriculture

Uncontrolled terms: 3D reconstruction? - ?Agricultural engineering technology? - ?Bibliometric? - ?Development dynamics? - ?Development trends? - ?Intelligentization? - ?International journals? - ?Multimodal sensing? - ?Smart agricultures? - ?Technology-based

Classification code: 214.1.2 Fatigue, Cracks and Fracture? - ?731.6 Robot Applications? - ?821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?821.2 Agricultural Machinery and Equipment? - ?821.4 Agricultural Methods? - ?903.2 Information Dissemination? - ?1101 Artificial Intelligence? - ?1101.2 Machine Learning? - ?1101.2.1 Deep Learning? - ?1105 Computer Networks? - ?1106.2 Data Handling and Data Processing? - ?1502 Environmental Engineering

Numerical data indexing: Percentage 1.80E+01%, Percentage 3.00E+01%, Percentage 4.461E+01%, Percentage 6.90E+01%, Percentage 7.536E+01%, Size 1.6256E+00m, Size 5.1181E+01m to 2.3114E+01m, Size 5.1181E+01m to 7.3152E+00m

DOI: 10.6041/j.issn.1000-1298.2025.10.022

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

65. Design and Experiment of Rockwool Plug Redirecting-Separating Device for Rockwool Plug Loader

Accession number: 20254419440387

Title of translation: 自动装钵机岩棉钵分离调向装置设计与试验

Authors: Gu, Song (1); Yan, Laiwang (1); Yang, Xu (1); Chu, Qi (2); Gu, Meizhang (2); Yang, Yanli (2); Liu, Guowei (3); Mu, Yinghui (1)

Author affiliation: (1) College of Engineering, South China Agricultural University, Guangzhou; 510642, China; (2) Guangzhou Sky Mechanical and Electrical Co., Ltd., Guangzhou; 510642, China; (3) Zhuhai Eponic Agriculture Technology Co., Ltd., Zhuhai; 519100, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 332-339 and 409

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In order to improve the efficiency of rock wool bowl loading, a method is proposed that uses a single layer supply of group rock wool bowls, flexible separation and orientation adjustment of inclined vertical conveyor belts, and combined output of horizontal rubber double circular belts. A rock wool bowl separation and orientation adjustment device is designed. Structural and parameter design were carried out for the rock wool bowl separation and orientation device, and operational performance tests were conducted. The speed of the directional belt of the device significantly affects the success rate of separation and directional adjustment, followed by the supply rate of the rock wool bowl, and the influence of the inclination angle of the directional belt is the smallest The separation and adjustment distance of the rock wool bowl in the separation and adjustment device is directly proportional to the supply rate of the rock wool bowl, and inversely proportional to the speed of the adjustment belt and the rubber circular belt The optimal operating parameters for the separation and fragrance mixing device are; α =75°, Q = 3 bowl/s, v1 =0. 16 m/s. At this time, the success rate of rock wool bowl separation and direction adjustment can reach 95%. After optimizing the structural parameters and operating parameters, the device can provide stable material supply for the rock wool bowl coiling machine and ensure its stable operation. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 23

Main heading: Wool

Controlled terms: Agricultural machinery? - ?Belt conveyors? - ?Mineral wool? - ?Rocks? - ?Rubber

Uncontrolled terms: Directional transport? - ?Group separation? - ?Operating parameters? - ?Rock wool? - ?Rockwool? - ?Rockwool plug? - ?Rockwool plug loader? - ?Single layer? - ?Supply rate

Classification code: 212.1 Natural Rubber? - ?213.2 Synthetic Fibers? - ?482.2 Rocks? - ?692.1 Conveyors? - ?821.2 Agricultural Machinery and Equipment? - ?821.5 Agricultural Products

Numerical data indexing: Percentage 9.50E+01%, Velocity 1.60E+01m/s

DOI: 10.6041/j.issn.1000-1298.2025.10.028

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

66. Evaluation of Well-facilitated Farmland Construction Grade Based on Multi-source Data and Fuzzy Clustering and Its Key Impact Factor Analysis

Accession number: 20254519444934

Title of translation: 基于多源数据和模糊聚类的高标准农田建设等级评定及其关键影响因素分析

Authors: Guo, Mengyu (1); Chen, Zheng (2); Tang, Huaizhi (1); Xia, Qiuyue (1); Mu, Qing (2); Zhou, Tong (2)

Author affiliation: (1) College of Land Science and Technology, China Agricultural University, Beijing; 100193, China; (2) Center oj Engineering and Construction Service, Ministry of Agriculture and Rural Affairs, Beijing; 100081, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 662-670

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: The evaluation of well-facilitated farmland construction was significant for clarifying the construction status of well-facilitated farmland and strengthening the supervision of well-facilitated farmland construction. However, problems such as diverse data sources and difficulty in unified application to evaluation were encountered. A comprehensive evaluation index system for well-facilitated farmland construction was constructed from the aspects of farmland infrastructure construction projects, farmland fertility improvement projects, and post-construction management. Based on multi-source data and fuzzy clustering, a multi-source data processing method system was applied in Southwest LP County. The results showed that fuzzy clustering was used to divide the comprehensive evaluation of well-facilitated farmland construction data into qualitative, quantitative, and intuitive data. Among them, the fuzzy processing method of qualitative data was usually a graded scoring method; the fuzzy processing methods of quantitative data included fuzzy membership function, minimum — maximum standardization, and so on. The fuzzy processing method of intuitive data was usually the grading scoring method after landscape index analysis. Most of the 80 evaluation units of well-facilitated farmland construction projects in Southwest LP County in 2019 were found to be at the third-class or above level, and the overall construction level was good. The comprehensive evaluation of single factor and well-facilitated farmland construction was shown to have certain representativeness. The quality of farmland infrastructure and fertility improvement projects was found to significantly impact the comprehensive evaluation of well-facilitated farmland construction. Five key factors affecting the comprehensive evaluation results were selected by the random forest model, including drainage capacity, irrigation capacity, cultivated field area, road accessibility, and field standardization degree, which constituted the minimum data set for the comprehensive evaluation of well-facilitated farmland construction. The research results provided a method reference for exploring the evaluation of well-facilitated farmland construction grade and key influencing factors suitable for large-scale nationwide application. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 30

Main heading: Membership functions

Controlled terms: Farms? - ?Function evaluation? - ?Fuzzy clustering? - ?Grading? - ?Project management? - ?Standardization

Uncontrolled terms: Comprehensive evaluation? - ?Fuzzy membership? - ?Fuzzy membership degree? - ?Grade estimations? - ?Membership degrees? - ?Multi-source data? - ?Multi-Sources? - ?Source data? - ?Southwest LP county? - ?Well-facilitated farmland

Classification code: 821 Agricultural Equipment and Methods; Vegetation and Pest Control? - ?902.2 Codes and Standards? - ?912.2 Management? - ?913.3 Quality Assurance and Control? - ?1101.2 Machine Learning? - ?1201 Mathematics? - ?1201.9 Numerical Methods

DOI: 10.6041/j.issn.1000-1298.2025.10.060

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

67. Low-carbon Layout Optimization of Silkworm Breeding Facilities Based on Multi-objective Optimization Algorithms

Accession number: 20254519444919

Title of translation: 基于多目标优化算法的桑蚕养殖基地低碳布局优化研究

Authors: Li, Shiyun (1); Li, Shini (1); Lu, Ruifeng (1); Pei, Zhi (1); Chen, Yong (1); Yi, Wenchao (1)

Author affiliation: (1) College of Mechanical Engineering, Zhcjiang University oj Technology, Hangzhou; 310023, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 704-715

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: With the global dissemination of low-carbon principles, the sericulture industry urgently needs to transition toward low-carbon operations, which requires its fundamental silkworm breeding facilities to improve both economic efficiency and environmental sustainability. Focusing on a specific silkworm breeding facility, aiming to address a multi-row layout problem with facilities of varying lengths and widths. It innovatively integrated silkworm survival rates and biological characteristics into the layout optimization framework, guided by low-carbon objectives, and a planning approach aimed at minimizing operational costs and maximizing non-logistical relationshipswas developed. To this end, an improved non-dominated sorting genetic algorithm — II (IMNSGA — II), incorporating dynamic mutation strategies and a simulated annealing local search mechanism, was proposed, which significantly improved search efficiency and convergence performance. The performance of IMNSGA — II was verified by the IGD index comparative experiment, and the layout examples were solved by IMNSGA — E . The experimental results demonstrated that the optimized scheme reduced operational costs by 29. 29%, enhanced non-logistical relationships by 10. 23%, and decreased the layout area by 29. 04% . Furthermore, the scheme strictly adhered to biosafety isolation standards by optimally positioning the cleaning and disinfection zone at the layout periphery, significantly mitigating cross-contamination risks, thereby providing robust scientific support for the low-carbon operation of silkworm breeding facilities. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 22

Main heading: Simulated annealing

Controlled terms: Biotic? - ?Carbon? - ?Carbon Economy? - ?Disinfection? - ?Genetic algorithms? - ?Invertebrates? - ?Multiobjective optimization? - ?Plant layout? - ?Screening? - ?Silk ? - ?Sustainable development

Uncontrolled terms: Enhanced NSGA — II? - ?Facilities layout? - ?Layout optimization? - ?Low carbon? - ?Low carbon planning? - ?Multi-objectives optimization? - ?Multi-row facility layout? - ?NSGA-II? - ?Silkworm survival rate? - ?Survival rate

Classification code: 103 Biology? - ?201.7.1 Heat Treatment Processes? - ?213.1 Natural Fibers? - ?802.3 Chemical Operations? - ?804 Chemical Products? - ?912 Industrial Engineering and Management? - ?1106 Computer Software, Data Handling and Applications? - ?1201.7 Optimization Techniques? - ?1501.1 Sustainable Development? - ?1502.2 Ecology and Ecosystems

Numerical data indexing: Percentage 2.30E+01%, Percentage 2.90E+01%, Percentage 4.00E+00%

DOI: 10.6041/j.issn.1000-1298.2025.10.064

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

68. Design and Multiphase Flow Simulation of Centrifugal Pump Negative-pressure Fish Suction System

Accession number: 20254519444930

Title of translation: 深远海养殖平台离心水泵式负压吸鱼系统设计与多相流模拟

Authors: Lin, Liqun (1, 2); Liu, Ping (1, 2); Zhang, Yaoming (1, 2); Xu, Zhiqiang (1, 2)

Author affiliation: (1) Fishery Machinery and Instrument Research Institute, Chinese Academy of Fishery Sciences, Shanghai; 200092, China; (2) Key Laboratory of Fishery Equipment and Engineering, Ministry of Agriculture and Rural Affairs, 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: 10

Issue date: October 2025

Publication year: 2025

Pages: 147-155

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: Aiming to address the challenge of efficiently harvesting live fish from large-scale aquaculture platforms in deep sea, where the yield per individual platform has exceeded 100 tons, a high-capacity fish suction system utilizing a centrifugal water pump to create a stable negative pressure was designed. Based on the ideal gas state equation, it was found that a pressure suitable for fish suction could be formed when the liquid level change was within 10. 3% of the total height of the fish collection tank. The key dimensions of the fish collection tank device were designed accordingly. A fish barrier grid model was constructed by using a porous medium model, and a three-dimensional transient numerical analysis of the fish suction process was performed based on the coupled VOF and CFD — DEM method. The numerical results showed that the liquid level remained basically constant during the fish suction process, with pressure fluctuations ranging from -37. 5 kPa to -26. 5 kPa, and the maximum pressure change rate was 0. 037 kPa/s, which was far below the fish damage threshold. The change in working pressure relative to the initial value of - 35 kPa was less than 7% . The distribution of fish schools in the collection tank showed significant differences with increasing flow rate; at 150 m /h, the fish initially clustered densely from the inlet to the outlet, then gradually dispersed; at 225 m /h, the higher flow rate caused faster dispersion to the tank periphery due to strong vortex effects, with more pronounced diffusion at higher velocities. The fish aggregation behavior at the fish barrier grid exhibited nonlinear changes with increasing flow rate; when the flow rate was 150 m /h, the number offish in contact with the fish barrier grid remained relatively stable; when the flow rate was 200 mVh, the number fluctuated intensely, with a peak of 55; when the flow rate was 225 m /h, non-steady-state fluctuations were significant, with 63 fish in contact with the fish barrier grid, accounting for 32% of the total number of contacts, indicating that an increase in flow rate significantly increased the risk of blockage. In order to further increase the suction capacity, it was necessary to optimize the structure of the fish interception grille by designing a curved grille, expanding the opening size, and adding fish guide channels to reduce the risk of blockage. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 26

Main heading: Flow rate

Controlled terms: Computational fluid dynamics? - ?Diffusion? - ?Equations of state of gases? - ?Fish? - ?Fisheries? - ?Numerical methods? - ?Numerical models? - ?Tanks (containers)? - ?Transient analysis

Uncontrolled terms: Aquaculture platform in deep sea? - ?Clogging prediction? - ?Deep sea? - ?Discrete element models? - ?Fish suction device? - ?Negative pressures? - ?Pressure fluctuation? - ?Suction devices? - ?Suction system? - ?Three-phase flow

Classification code: 301.1 Fluid Flow? - ?301.1.4 Computational Fluid Dynamics? - ?302 Thermodynamics and Heat Transfer? - ?471.5 Sea as Source of Minerals and Food? - ?610.2 Tanks and Accessories? - ?822 Food Technology? - ?822.3 Food Products? - ?941.5 Mechanical Variables Measurements? - ?1201.4 Applied Mathematics? - ?1201.9 Numerical Methods? - ?1301.1.2 Physical Properties of Gases, Liquids and Solids

Numerical data indexing: Mass 1.00E+05kg, Percentage 3.00E+00%, Percentage 3.20E+01%, Percentage 7.00E+00%, Pressure -3.50E+04Pa, Pressure 3.70E+04Pa, Pressure 5.00E+03Pa, Size 1.50E+02m, Size 2.25E+02m

DOI: 10.6041/j.issn.1000-1298.2025.10.015

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village

69. Mobile Chassis Design and Passability of Arched Waist Agricultural Robot

Accession number: 20254419440392

Title of translation: 拱腰式农业机器人移动底盘设计与通过性研究

Authors: Nie, Jianjun (1); Xia, Kongtao (1); Xie, Xiaolin (2); Lü, Yalei (3); Li, Caimin (1)

Author affiliation: (1) School of Intelligent Mechatronies Engineering, Zhongyuan University of Technology, Zhengzhou; 451191, China; (2) College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang; 471000, China; (3) Henan Zhongpingdian New Energy Investment Co., Ltd., Pingdingshan; 467000, China

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

Abbreviated source title: Nongye Jixie Xuebao

Volume: 56

Issue: 10

Issue date: October 2025

Publication year: 2025

Pages: 736-745

Language: Chinese

ISSN: 10001298

CODEN: NUYCA3

Document type: Journal article (JA)

Publisher: Chinese Society of Agricultural Machinery

Abstract: In view of the diverse and complex farming environment in hilly and mountainous areas, traditional machinery is difficult to adapt to the harsh farming environment, a mobile chassis of an arch waist agricultural robot with adjustable track posture was designed. By adjusting the posture of the track traction device, the robot increased the contact pressure between the track and the road surface, and improved the passing performance of the unstructured terrain. Firstly, the structure of robot mobile chassis, design of transmission system and track traction device were described in detail, the relationship between the track lifting, arching angle and the rotation angle of the output shaft of the worm gear reducer was analyzed by analytical method, the maximum lifting angle and arching angle of the track were 22. 87° and 20. 13°, respectively. Secondly, the passing performance of the robot chassis was analyzed, using the law of robot centroid and track attitude change, the relationship between track attitude and obstacle height was obtained, the maximum obstacle height of the robot was 232. 85 mm by Matlab calculation. Through climbing and steering tests, it was obtained that the robot can climb a 30° slope with a minimum turning radius of 805 mm under a load of 150 kg. At the same time, Adams software was used to simulate the robot’s track lifting and arching action, the relationship between the crawler attitude and the rocker angle was obtained. Finally, the reliability of the theoretical analysis was verified by the prototype test. The experiment showed that the designed robot chassis can improve the passing performance of the robot after adjusting the attitude, with a variety of agricultural tools, it can be well adapted to the complex hilly and mountainous farming environment, which provided a favorable reference for the development of agricultural mechanization in hilly and mountainous areas. ? 2025 Chinese Society of Agricultural Machinery. All rights reserved.

Number of references: 28

Main heading: Machine design

Controlled terms: Agricultural implements? - ?Agricultural robots? - ?Chassis? - ?MATLAB? - ?Transmissions

Uncontrolled terms: Adam? - ?Agricultural robot? - ?Chassis design? - ?Hilly and mountainous areas? - ?Mobile chassi? - ?Obstacle performance? - ?Performance? - ?Posture adjustment? - ?Robot chassis? - ?Traction devices

Classification code: 601 Mechanical Design? - ?602.2 Mechanical Transmissions? - ?662.3 Automobile Components and Materials? - ?731.6 Robot Applications? - ?821.2 Agricultural Machinery and Equipment? - ?904 Design? - ?1106.5 Computer Applications? - ?1201.5 Computational Mathematics

Numerical data indexing: Mass 1.50E+02kg, Size 8.05E-01m, Size 8.50E-02m

DOI: 10.6041/j.issn.1000-1298.2025.10.067

Compendex references: YES

Database: Compendex

Data Provider: Engineering Village