  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.
