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Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints
Arun N. Sivakumar, Mateus V. Gasparino, Michael McGuire, Vitor A · 2024-04-27 · via cs.AI updates on arXiv.org

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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