Towards Autonomous Crop Monitoring: Inserting Sensors in Cluttered Environments

Abstract

Monitoring crop nutrients can aid farmers in optimizing fertilizer use. Many existing robots rely on visionbased phenotyping, however, which can only indirectly estimate nutrient deficiencies once crops have undergone visible color changes. We present a contact-based phenotyping robot platform that can directly insert nitrate sensors into cornstalks to
proactively monitor macronutrient levels in crops. This task is challenging because inserting such sensors requires subcentimeter 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 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 insights obtained from deployment in the field. Our research platform is open-sourced, with additional information available at https://kantor-lab.github.io/cornbot.