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Abstract

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.

Available Versions of this Item

• Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots. (deposited 05 Sep 2023 11:54)
  • Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots. (deposited 14 Aug 2023 15:06) [Currently Displayed]