Intelligent intra-row robotic weeding system combining deep learning technology with a targeted weeding mode. (April 2022)

• Record Type: Journal Article
• Authors: Quan, Longzhe; Jiang, Wei; Li, Hailong; Li, Hengda; Wang, Qi; Chen, Liqing
• Is Part Of: Biosystems engineering. Volume 216(2022)
• Page Start: 13
• Page End: 31
• Publication Date: 2022-04
• Subjects: Robotic weeding -- Intra-row weed control -- Organic agriculture -- Precision agriculture -- Deep learning; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2022.01.019
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Mechanical weed control is the most promising weed control method for weed management in organic agriculture. Among the various available methods, inter-row weeding technology is relatively mature, and researchers have focused on intra-row weeding technology. The objective of this study was to develop a new intelligent intra-row mechanical robotic weeding system based on deep learning for crop and weed detection. The system consists of a mobile robot platform and two intelligent weeding units. The mobile robot platform provides power support as well as operating conditions for the weeding units. A targeted weeding pattern was proposed based on the deep learning detection results, and protected and targeted weeding zones were established by strict criteria to reduce crop injury rates. In addition, three weed control knives were designed according to different field environments. Field trials showed that among the three weed knives, the plough-surface weeding knife was the most effective and most suitable knife for flat cultivation, and the wedge weeding knife was most suitable for ridge planting. Based on the obtained results, the best weeding method was determined, and the experiment was repeated using a wedge weeding knife under ridge planting conditions. The final weed removal rate was 85.91%, and the crop injury rate was 1.17%. The results demonstrated the feasibility of the proposed intra-row weed control method. Highlights: A new type of vertical rotary mechanical weeding system was developed. Detection is conducted with deep learning techniques to create protected and weeded areas. A targeted weed control method with less soil surface disturbance is proposed. Three kinds of weeding knives are designed for different field conditions. The weed removal rate of the robot is 85.91%, and the crop damage rate was 1.17%.