Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints

Arun N. Sivakumar, Mateus V. Gasparino, Michael McGuire, Vitor A. H. Higuti, M. Ugur Akcal +1 more
4/26/2024
cs.ROcs.AIcs.CV

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Abstract

We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows ($\sim 0.75$ m), degradation in RTK-GPS accuracy due to multipath error, and noise in LiDAR measurements from the excessive clutter. Our system, CropFollow++, introduces modular and interpretable perception architecture with a learned semantic keypoint representation. We deployed CropFollow++ in multiple under-canopy cover crop planting robots on a large scale (25 km in total) in various field conditions and we discuss the key lessons learned from this.

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Cite this paper

@article{sivakumar2024lessons,
  title  = {Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints},
  author = {Arun N. Sivakumar and Mateus V. Gasparino and Michael McGuire and Vitor A. H. Higuti and M. Ugur Akcal and Girish Chowdhary},
  year   = {2024},
  eprint = {2404.17718},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.17718v1}
}

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