Research on mulberry leaf disease recognition method based on deep learning

• Published in: Diànzǐ jìshù yīngyòng Vol. 51; no. 3; pp. 70 - 76
• Main Authors: Ye Hui, Xiang Donghui, Zeng Songwei
• Format: Journal Article
• Language: Chinese
• Published: National Computer System Engineering Research Institute of China 01.03.2025
• Subjects: model improvement; mulberry leaf disease; object detection; yolov8
• ISSN: 0258-7998
• DOI: 10.16157/j.issn.0258-7998.245509

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.