A Low-Cost Co-Designed Robotic System for Selective Sweet Pepper Harvesting with Edge-Computed Perception and Decision-Making

Existing selective sweet pepper harvesting systems use industrial arms and high-performance computers, which limits adoption on small farms and replication by researchers. We developed a low-cost, open-source modular harvesting robot based on Raspberry Pi 5 and a 3D-printed manipulator. The system has three modules. First, an improved YOLOv13n detector combines GhostConv, ReLU6, and DIoU Loss. It reaches 96.9% mAP@0.5 for fruits and 95.1% for peduncles with 2.35 M parameters. Compared with the YOLOv13n baseline, parameters and GFLOPs drop by 4.1% and 3.2%, while overall mAP@0.5 falls by 1.8 percentage points. Second, an adaptive size measurement and multi-criteria decision strategy uses the correlation between median depth and tilt angle (R2 = 0.824), giving a mean absolute error of 5.0 mm and a 95.4% effective hit rate. Third, a ROS 2-based full-stack platform enables closed-loop perception–decision–execution. Laboratory tests give a selected-target harvesting success rate of 95.4% (103/108) and an overall success rate of 85.8% (103/120), with 1.9% fruit damage. Motion planning and execution consume 83% of cycle time. All hardware designs, ROS 2 packages, and model weights are released.