Fruit Counting

Automated fruit counting is a rapidly developing field aiming to improve efficiency and accuracy in agriculture through computer vision. Current research focuses on developing robust algorithms, often employing deep learning architectures like YOLO and U-Net variants, to detect and count fruits in images and videos captured by various sensors (RGB cameras, LiDAR, etc.), addressing challenges like occlusion and varying fruit sizes and densities. These advancements offer significant potential for optimizing resource management, yield prediction, and harvest planning, ultimately leading to increased productivity and reduced labor costs in the agricultural sector.