Research on mulberry leaf disease recognition method based on deep learning

To improve the accuracy of mulberry leaf disease detection and enable convenient and rapid deployment of models on mobile devices, an improved version of the YOLOv8 model, named YOLOv8-Evo, is proposed to address issues such as small lesion spots and complex backgrounds in natural environments. The algorithm introduces a deformable convolution module within the Backbone to capture disease details and shapes more flexibly. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated into the Neck to highlight key features and regions in the image. After validation on a dataset of 18 849 mulberry leaf disease images, the YOLOv8-Evo model demonstrates a 2.4% increase in precision, a 1.5% increase in recall rate, a 1% improvement in mAP50, and a 0.7% improvement in mAP50-95 compared to the YOLOv8s model. These results provide both theoretical support and technical backing for the automation of mulberry leaf disease identification.