Orchard Environment

Orchard environment research focuses on developing automated systems for tasks like fruit detection, counting, and size estimation, addressing labor shortages and improving efficiency in fruit production. Current research heavily utilizes computer vision techniques, employing deep learning models such as YOLO (various versions) and Mask R-CNN, often combined with 3D point cloud data and shape fitting for precise measurements. These advancements enable improved yield prediction, precision agriculture practices (like targeted pollination and thinning), and robotic automation of harvesting and other orchard operations, impacting both agricultural productivity and the development of advanced robotics in challenging environments.