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

arXiv: https://arxiv.org/abs/2404.17718 · HTML: https://mlsanity.com/paper/2404.17718/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 1 of 1)

- [Learned Visual Navigation for Under-Canopy Agricultural Robots](https://mlsanity.com/paper/2107.02792/) (2021)

## Built on by (most cited first, 3 of 3)

- [ZeST: A VLM-based Zero-Shot Traversability Navigation for Unknown Environments](https://mlsanity.com/paper/2508.19131/) (2025)
- [RowDetr: End-to-End Crop Row Detection Using Polynomials](https://mlsanity.com/paper/2412.10525/) (2024)
- [Language-Guided Object Search in Agricultural Environments](https://mlsanity.com/paper/2503.01068/) (2025)

## Abstract (arXiv)

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 (~ 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.