AI-Driven Closed-Loop Agricultural Robotics for Precision Phenotyping and Autonomous Selective Harvesting

Abstract

This paper addresses the persistent gap between robotic crop monitoring and autonomous harvesting by proposing an integrated, closed-loop agricultural system that unifies real-time AI-driven phenotyping with selective fruit harvesting in a single operational framework. We developed and field-evaluated AgriLoop-1, a unified robotic platform combining a multi-modal perception suite, a compliant end-effector for damage-aware fruit handling, and a co-designed AI software architecture. The perception pipeline leverages a YOLOv8-Seg model to perform simultaneous fruit detection, instance segmentation, ripeness classification, and yield estimation, while a dynamic Harvestability Score guide’s target prioritization and optimized picking sequences based on spatial, phenotypic, and reachability constraints. Field trials conducted in a commercial apple orchard demonstrated reliable real-world performance, achieving an operational success rate of 87.0% and an effective harvesting throughput of 122 fruits per hour over 15 hours of continuous deployment. The system maintained stable closed-loop operation under varying environmental conditions, confirming the robustness of the integrated perception–decision–action architecture. This study presents a field-validated robotic system that effectively closes the perception–action loop in agricultural robotics. By tightly integrating precision phenotyping with adaptive harvesting decisions, the proposed framework moves beyond conventional modular approaches and establishes a practical foundation for intelligent, data-driven crop management systems capable of addressing labor shortages and supporting sustainable agricultural production.