Robotic weed control using automated weed and crop classification

Xiaolong Wu (1) , Stéphanie Aravecchia (1) , Philipp Lottes (2) , Cyrill Stachniss (2) , Cédric Pradalier (1)

1 Georgia Tech Lorraine [Metz]
2 Universität Bonn = University of Bonn

Autonomous robotic weeding systems in precision farming have demonstrated their full potential to alleviate the current dependency on agrochemicals such as herbicides and pesticides, thus reducing environmental pollution and improving sustainability. However, most previous works require fast and constant-time weed detection systems to achieve real-time treatment, which forecloses the implementation of more capable but time-consuming algorithms, e.g. learning-based methods. In this paper, a non-overlapping multi-camera system is applied to provide flexibility for the weed control system in dealing with the indeterminate classification delays. The design, implementation, and testing of our proposed modular weed control unit with mechanical and chemical weeding tools are presented. A framework that performs naive Bayes filtering, 3D direct intra-and inter-camera visual tracking, and predictive control, while integrating state-of-the-art crop/weed detection algorithms, is developed to guide the tools to achieve high-precision weed removal. The experimental results show that our proposed fully operational weed control system is capable of performing selective mechanical as well as chemical in-row weeding with indeterminate detection delays in different terrain conditions and crop growth stages.

Domaines: Robotique [cs.RO]

Xiaolong Wu, Stéphanie Aravecchia, Philipp Lottes, Cyrill Stachniss, Cédric Pradalier. Robotic weed control using automated weed and crop classification. 2020. ⟨hal-02484462⟩