SL-YOLO: Rapid Detection Network for Gannan Navel Orange Disease Detection

Aced with the huge adverse impact of crop diseases, the development of efficient and accurate disease detection technologies is an important prerequisite for the sustainable development of agriculture. Among the numerous detection methods for various crops, this paper proposes a method SL-YOLO for disease target detection of gannan navel orange. SL-YOLO consists of a backbone, a neck, and a head. The backbone module is mainly used to extract feature information, while the neck module is mainly used for feature fusion to facilitate the follow-up detection of the head module. The head module mainly uses the fused neck information to carry out multi-scale detection and generate the result map. The experiment proves that our network can effectively improve the detection speed while ensuring the accuracy.