Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots
This addresses the problem of high-cost navigation for agri-robots by providing a vision-based solution, though it is incremental as it builds on existing deep learning methods for a specific domain.
The paper tackled autonomous navigation in agricultural fields by developing a robust crop row detection algorithm using inexpensive cameras, which outperformed a baseline in challenging conditions and was tested for visual servoing in simulation.
Autonomous navigation in agricultural environments is challenged by varying field conditions that arise in arable fields. State-of-the-art solutions for autonomous navigation in such environments require expensive hardware such as RTK-GNSS. This paper presents a robust crop row detection algorithm that withstands such field variations using inexpensive cameras. Existing datasets for crop row detection does not represent all the possible field variations. A dataset of sugar beet images was created representing 11 field variations comprised of multiple grow stages, light levels, varying weed densities, curved crop rows and discontinuous crop rows. The proposed pipeline segments the crop rows using a deep learning-based method and employs the predicted segmentation mask for extraction of the central crop using a novel central crop row selection algorithm. The novel crop row detection algorithm was tested for crop row detection performance and the capability of visual servoing along a crop row. The visual servoing-based navigation was tested on a realistic simulation scenario with the real ground and plant textures. Our algorithm demonstrated robust vision-based crop row detection in challenging field conditions outperforming the baseline.