An improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition

Abstract

Apple leaf lesions present a challenge for their detection and recognition because of their wide variety of species, morphologies, uneven sizes, and complex backgrounds. This paper proposes an improved multi‐scale YOLOv8 for apple leaf dense lesion detection and recognition. In the proposed YOLOv8, an improved C2f‐RFEM module is constructed in the backbone network to improve the feature extraction of disease object. A new neck network is designed by using C2f‐DCN and C2f‐DCN‐EMA module, which are established with deformable convolutions and efficient multi‐scale attention module with cross‐spatial learning attention mechanism. Moreover, a large‐scale detection head is introduced for increasing the resolution of the small lesion targets, so as to further improve the detection ability for multi‐scale diseases. Finally, the improved YOLOv8 is tested on the common objects in context (COCO) database with 80 kinds of objectives and an apple leaf disease database with 8 kinds of diseases. Compared to the baseline YOLOv8 model, the proposed improved YOLOv8 increases the mAP0.5 by 3%, and decreases the floating‐point operations per second (FLOPs) by 0.3G on the COCO database. For the apple leaf disease database, the improved YOLOv8 outperforms in terms of mAP and FLOPs compared to other models, for parameters and model size, it is ranked second and third, respectively. Experimental results show that the improved YOLOv8 has better adaptability to multi‐scale dense distribution of apple leaf disease spots with complex scenarios.