Robotic Harvesting

Robotic harvesting aims to automate fruit and vegetable picking, addressing labor shortages and increasing agricultural efficiency. Current research heavily utilizes computer vision, employing deep learning models like YOLOv5 and other custom architectures for real-time object detection and pose estimation, often incorporating LiDAR data for improved accuracy. These advancements focus on improving picking speed, accuracy, and robustness in challenging environments (e.g., dense foliage, varying lighting), with a particular emphasis on developing specialized grippers for delicate crops. Successful implementation of these technologies promises significant improvements in agricultural productivity and sustainability.