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Autonomous Vision-Based Navigation and Control for Intra-Row Weeding

For agriculture to become more sustainable, new practices and new cropping systems are needed to limit inputs such as fertilisers and phytosanitary products. This paper exposes an autonomous vision-based approach for navigation and intra-row weeding. To preserve crops (here concerned with maize and bean), a plant classification algorithm using easy-to-extract features has been developed and integrated on a real agricultural system. The approach uses frontal and proximal detection; the first allows for autonomous vision-based navigation without relying on GPS; and the latter regulates the weeding tool action. Realistic simulation scenarios are presented in order to validate the proposed approach as well as open-field experiments in real agricultural conditions obtained within the framework and evaluation campaigns of the ROSE challenge.