Design and Experiment of an Automatic Row-Oriented Spraying System Based on Machine Vision for Early-Stage Maize Corps

Kang Zheng, Xueguan Zhao, Changjie Han, Yakai He, Changyuan Zhai () and Chunjiang Zhao ()
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Kang Zheng: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Xueguan Zhao: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Changjie Han: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Yakai He: Chinese Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China
Changyuan Zhai: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Chunjiang Zhao: Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Agriculture, 2023, vol. 13, issue 3, 1-22

Abstract: Spraying pesticides using row alignment in the maize seedling stage can effectively improve pesticide utilization and protect the ecological environment. Therefore, this study extracts a guidance line for maize crops using machine vision and develops an automatic row-oriented control system based on a high-clearance sprayer. First, the feature points of crop rows are extracted using a vertical projection method. Second, the candidate crop rows are obtained using a Hough transform, and two auxiliary line extraction methods for crop rows based on the slope feature outlier algorithm are proposed. Then, the guidance line of the crop rows is fitted using a tangent formula. To greatly improve the robustness of the vision algorithm, a Kalman filter is used to estimate and optimize the guidance line to obtain the guidance parameters. Finally, a visual row-oriented spraying platform based on autonomous navigation is built, and the row alignment accuracy and spraying performance are tested. The experimental results showed that, when autonomous navigation is turned on, the average algorithm time consumption of guidance line detection is 42 ms, the optimal recognition accuracy is 93.3%, the average deviation error of simulated crop rows is 3.2 cm and that of field crop rows is 4.36 cm. The test results meet the requirements of an automatic row-oriented control system, and it was found that the accuracy of row alignment decreased with increasing vehicle speed. The innovative spray performance test found that compared with the traditional spray, the inter-row pesticide savings were 20.4% and 11.4% overall, and the application performance was significantly improved.

Keywords: maize; machine vision; pesticide application; navigation tracking (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: 2023
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