Towards Autonomous Crop Monitoring: Inserting Sensors in Cluttered Environments

Abstract-- 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 perceptionaction 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. With the development of artificial intelligence in computer vision and robotics, the agricultural sector is poised to implement precision agriculture methods to enhance crop production efficiency and minimize environmental footprint [1]. Figure 1: Robot inserting sensors into cornstalks to monitor plant nitrate concentration in Curtiss Farm, Iowa.