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 vision-based 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 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 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 <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://kantor-lab.github.io/cornbot</uri> .