The automation of fruit harvesting has become an important research topic in precision agriculture due to increasing labor shortages, rising production costs, and the need for improved harvesting efficiency. Among horticultural crops, strawberries present particular challenges for robotic harvesting because of their variability in size, shape, ripeness, and frequent occlusions caused by leaves and surrounding fruit. The objective of this work is to demonstrate the feasibility of a reproducible perception-to-manipulation framework for robotic strawberry harvesting based on commercially available hardware and established computer vision techniques, rather than to propose a novel object detection algorithm. The proposed system integrates a YOLOv3-based (You Only Look Once) object detector, monocular vision for fruit localization, and a Universal Robots UR5e collaborative manipulator. Strawberry coordinates estimated from monocular images are transformed into the robot reference frame and transmitted through the XML-RPC (Extensible Markup Language-Remote Procedure Call) protocol, enabling robot positioning. The system was experimentally validated in a controlled indoor environment under different artificial illumination conditions. The YOLOv3 detector achieved a mAP0.5:0.95 of 37.4%, a precision of 84.2%, a recall of 76.1%, and a latency of 6.5 ms per image (153.8 FPS). The experiments also demonstrated reliable communication between the perception and robotic manipulation modules, enabling the robotic arm to reach the estimated strawberry positions. The proposed framework provides a practical and low-cost solution for integrating deep-learning-based perception with robotic manipulation and establishes a solid basis for future work on localization accuracy, automated grasping, harvesting efficiency, and deployment in real agricultural environments.
Campoamor et al. (Wed,) studied this question.