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

Jing He, Jiarui Zou, Zhun Cheng, Jiatao Huang, Runmao Zhao, Guoqing Wang and Jie He ()
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Jing He: School of Mechanical and Electrical Engineering, Guangdong Polytechnic of Industry and Commerce, 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
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
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

Agriculture, 2025, vol. 15, issue 18, 1-15

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.

Keywords: area division; human–machine operative mode; operation score (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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