# Towards Autonomous Crop Monitoring: Inserting Sensors in Cluttered Environments

arXiv: https://arxiv.org/abs/2311.03697 · HTML: https://mlsanity.com/paper/2311.03697/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2023.

## Built on by (most cited first, 2 of 2)

- [RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing](https://mlsanity.com/paper/2501.10621/) (2025)
- [BEACON: Belief-Enabled Adaptive CONtrol for Imitation Learning under Uncertainty](https://mlsanity.com/paper/2609.22730/) (2026)

## Abstract (arXiv)

We present a contact-based phenotyping robot platform that can autonomously insert nitrate sensors into cornstalks to proactively monitor macronutrient levels in crops. This task is challenging because inserting such sensors requires sub-centimeter precision in an environment which contains high levels of clutter, lighting variation, and occlusion. To address these challenges, we develop a robust perception-action pipeline to detect and grasp stalks, and create a custom robot gripper which mechanically aligns the sensor before inserting it into the stalk. Through experimental validation on 48 unique stalks in a cornfield in Iowa, we demonstrate our platform's capability of detecting a stalk with 94% success, grasping a stalk with 90% success, and inserting a sensor with 60% success. In addition to developing an autonomous phenotyping research platform, we share key challenges and insights obtained from deployment in the field. Our research platform is open-sourced, with additional information available at https://kantor-lab.github.io/cornbot.