  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.
