Robotic Harvesting of Apples Using ROS2

Rising global food demand, increasing labor costs, and persistent labor shortages have created significant challenges for specialty crop production, particularly in la-bor-intensive tasks such as fruit harvesting. Robotic harvesting offers a promising long-term solution, yet its adoption in orchard environments remains limited due to unstructured conditions, variable lighting, and difficulties in fruit recognition and ma-nipulation. This study presents an improved robotic fruit harvesting system, Orchard roBot (OrBot), developed by the Robotics Vision Lab at Northwest Nazarene University, with the goal of advancing autonomous apple harvesting toward greater practicality and economic viability. The updated OrBot platform integrates a dual-camera vision system consisting of an eye-to-hand stereo camera with a wide field of view for fruit target detection and an eye-in-hand RGB-D camera for precise manipulation. The con-trol architecture was redesigned using Robot Operating System 2 (ROS2) and Python, enabling modular subsystem development and improved system coordination. Fruit detection was performed using a YOLOv5 deep learning model, and visual servoing was employed to guide the robotic manipulator toward the target fruit. System performance was evaluated through laboratory experiments using artificial trees and field tests conducted in a commercial apple orchard in Idaho. OrBot achieved a 100% harvesting success rate in indoor tests and a 75–80% success rate in outdoor orchard conditions, with improved performance observed following orchard pruning. Experimental results demonstrate that the dual-camera approach significantly enhances fruit search effi-ciency and harvesting reliability. Identified limitations include sensitivity to lighting conditions, end effector performance with varying fruit sizes, and depth estimation errors. Overall, the results indicate that OrBot represents a meaningful step toward ef-fective robotic fruit harvesting and highlights key areas for future improvement in vi-sion, manipulation, and system robustness.