Journal
JOURNAL OF FIELD ROBOTICS
Volume 37, Issue 2, Pages 322-340Publisher
WILEY
DOI: 10.1002/rob.21938
Keywords
agriculture; computer vision; terrestrial robotics
Categories
Funding
- European Commission [H2020-ICT-644227-FLOURISH]
- Deutsche Forschungsgemeinschaft [EXC-2070-390732324]
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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, for example, learning-based methods. In this paper, a nonoverlapping multicamera 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.
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