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.

翻译：我们提出了一种基于接触的表型机器人平台，该平台能够自主将硝酸盐传感器插入玉米秆中，以主动监测作物的常量营养素水平。这项任务极具挑战性，因为插入此类传感器需要亚厘米级的精度，而环境中存在高度杂乱、光照变化和遮挡。为解决这些挑战，我们开发了一个稳健的感知-行动流水线，用于检测和抓取玉米秆，并设计了一种定制机器人夹爪，可在将传感器插入玉米秆前实现机械对齐。通过在爱荷华州玉米田对48个不同玉米秆进行的实验验证，我们展示了该平台的能力：检测玉米秆的成功率为94%，抓取成功率为90%，传感器插入成功率为60%。除了开发自主表型研究平台外，我们还分享了在田间部署中遇到的关键挑战与洞见。我们的研究平台已开源，更多信息请访问 https://kantor-lab.github.io/cornbot。