Fruit Detection

Fruit detection research focuses on automating the identification and localization of fruits in agricultural settings, primarily to improve efficiency and yield estimation in harvesting and crop management. Current efforts concentrate on developing robust and efficient algorithms, often employing deep learning architectures like YOLO and its variants, along with Neural Radiance Fields (NeRFs), to address challenges such as occlusion, varying lighting conditions, and the need for real-time processing on low-power devices. These advancements have significant implications for precision agriculture, enabling automated harvesting, yield prediction, and improved resource allocation, ultimately contributing to increased productivity and reduced labor costs.