Deep learning based weed detection and target spraying robot system at seedling stage of cotton field

Abstract

The precision spraying robot dispensing herbicides only on unwanted plants based on machine vision detection is the most appropriate approach to ensure the sustainable agro-ecosystem and the minimum impact of nuisance weeds. However, the coexistence of crops and weeds, the similarities of plants and the multi-scale attribute of weeds make reliable detection difficult, leading to serious limitations in the application of deep learning method to target spraying in the field environment. In this paper, 4694 representative images are acquired from cotton field scenario as the data basis for deep learning model. A novel weed detection model is constructed by employing CBAM module, BiFPN structure and Bilinear interpolation algorithm. The proposed network can effectively learn the deep information and distinguish weeds from cotton seedlings in various complicated growth states. Evaluation experiments on our constructed dataset indicate that the proposed method reaches an mAP of 98.43% with faster inference speed than Faster R-CNN. Our proposed weed detection model is also deployed in spraying robot that we developed ourselves, and field trials are conducted for detection and spraying, which could maintain the excellent performance with mAP of 97.42% and effective spraying rate of 98.93%. The ability to successfully execute the weed detection and herbicide spraying management in the field lays foundation for targeted spraying in precision weed control, which has an excellent impact on cotton cultivation and growth.