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Methods of Work Area Division Under a Human–Machine Cooperative Mode of Intelligent Agricultural Machinery Equipment

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  • Jing He

    (School of Mechanical and Electrical Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510642, China
    Key Laboratory of Key Technology on Agricultural Machine and Equipment (South China Agricultural University), Ministry of Education, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China)

  • Jiarui Zou

    (Key Laboratory of Key Technology on Agricultural Machine and Equipment (South China Agricultural University), Ministry of Education, Guangzhou 510642, China
    Institute of Intelligent Manufacturing, Hunan Financial & Industrial Vocational-Technical College, Hengyang 421200, China)

  • Zhun Cheng

    (School of Mechanical and Electrical Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510642, China)

  • Jiatao Huang

    (Key Laboratory of Key Technology on Agricultural Machine and Equipment (South China Agricultural University), Ministry of Education, Guangzhou 510642, China)

  • Runmao Zhao

    (Key Laboratory of Key Technology on Agricultural Machine and Equipment (South China Agricultural University), Ministry of Education, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China)

  • Guoqing Wang

    (Ji’an City Crop Breeding Farm, Ji’an 343000, China)

  • Jie He

    (Key Laboratory of Key Technology on Agricultural Machine and Equipment (South China Agricultural University), Ministry of Education, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China)

Abstract

To address the problems of incomplete coverage of complex plots and low efficiency in unmanned agricultural machinery operations, the study proposes the Human–Machine Collaboration (HMC). Targeting different types of plots, the study designed the method of area division based on the Breseham algorithm and the polygonal clipping algorithm. In addition, the study proposed a secondary division method of the area based on alternating point judgment and risk area evaluation function to ensure the security of the HMC. The experimental results show that the coverage rate of HMC is 100% and the field operation work efficiency is higher than 86% under different shapes of plots (rectangle, right trapezoid and ordinary quadrilateral). In the three shapes of plots, the operation scores of the HMC in the open edge area are 96.08, 163.39, and 137.4, respectively; the operation scores in other areas are 104.73, 89.88, 97.77, respectively; and the comprehensive scores are 162.36, 204.33, and 189.85, respectively, which are higher than those of unmanned operation and manned operation, showing comparatively better performance. The area division under the HMC meets the operational requirements, and the research provides technical support for unmanned farm development.

Suggested Citation

  • Jing He & Jiarui Zou & Zhun Cheng & Jiatao Huang & Runmao Zhao & Guoqing Wang & Jie He, 2025. "Methods of Work Area Division Under a Human–Machine Cooperative Mode of Intelligent Agricultural Machinery Equipment," Agriculture, MDPI, vol. 15(18), pages 1-15, September.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:18:p:1919-:d:1746493
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    References listed on IDEAS

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    1. Jiamuyang Zhao & Shuxiang Fan & Baohua Zhang & Aichen Wang & Liyuan Zhang & Qingzhen Zhu, 2025. "Research Status and Development Trends of Deep Reinforcement Learning in the Intelligent Transformation of Agricultural Machinery," Agriculture, MDPI, vol. 15(11), pages 1-25, June.
    2. Fan Zhang & Wenyu Zhang & Xiwen Luo & Zhigang Zhang & Yueteng Lu & Ben Wang, 2022. "Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems," Agriculture, MDPI, vol. 12(2), pages 1-19, February.
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