爆走黑料

This project focuses on developing a computer vision system for detecting and measuring fruit on trees using deep learning techniques. Accurate on-tree fruit detection and size estimation is vital for yield prediction and efficient harvest planning, especially in large-scale orchards where manual counting is impractical. You will begin by adapting state-of-the-art object detection models (e.g., YOLOv5) to detect fruit in challenging conditions鈥攕uch as partial occlusion, varying viewpoints, and changes in distance from the camera. The project will then explore object segmentation techniques, which aim to infer the full shape of fruits even when they are partially hidden by leaves or branches. RGB-D datasets (with depth information) will also be used to improve size estimation accuracy. This work has real-world applications in precision agriculture and contributes to building automated, non-invasive fruit monitoring systems.