Strawberry-harvesting Robot System with Object Detection Based on YOLO

Abstract

Recently, interest in intelligent agricultural robots has surged due to the aging of rural workers, leading to active research on agricultural automation. We designed a strawberry harvesting robot that moves along a rail, equipped with a 3-axis linear actuator, an RGB-Depth camera for object detection, and a rotary gripper for branch cutting. Our research focused on developing algorithms for strawberry maturity classification using an AI vision system, calculating cutting points for fruit acquisition, and implementing these algorithms in a robot. A convolutional neural network based on YOLO was employed for object detection, and representation learning was used to determine the picking point with an ROI(Region Of Interest) image derived from object detection. Our strawberry harvesting robot system boasts an average harvesting success rate of 90% for ripe fruits.