YOLOv8-RBean: Runner Bean Leaf Disease Detection Model Based on YOLOv8 - Hongbing Chen; Haoting Zhai; Jinghuan Hu; Hongrui Chen; Changji Wen; Yizhe Feng; Kun Wang; Zhipeng Li; Guangyao Wang (Agronomy)
Runner bean is an important food source worldwide, and effective disease prevention and control are crucial to ensuring food security. However, runner bean is vulnerable to various diseases during its growth, which significantly affect both yield and quality. Despite the continuous advancement of disease detection technologies, existing legume disease detection models still face significant challenges in identifying small-scale, irregular, and visually insignificant disease types, limiting their practical application. To address this issue, this study proposes an improved detection model, YOLOv8_RBean, based on the YOLOv8n object detection framework, specifically designed for runner bean leaf disease detection. The model enhances detection performance through three key innovations: (1) the BeanConv module, which integrates depthwise separable convolution and pointwise convolution to improve multi-scale feature extraction; (2) a lightweight LA attention mechanism that incorporates spatial, channel, and coordinate information to enhance feature representation; and (3) a lightweight BLBlock structure built upon DWConv and LA attention, which optimizes computational efficiency while maintaining high accuracy. Experimental results on the runner bean disease dataset demonstrate that the proposed model achieves a precision of 88.7%, with mAP50 and mAP50-95 reaching 83.5% and 71.3%, respectively. Moreover, the model reduces the number of parameters to 2.71 M and computational cost to 7.5 GFLOPs, representing reductions of 10% and 7.4% compared to the baseline model. Notably, the method shows clear advantages in detecting morphologically subtle diseases such as viral infections, providing an efficient and practical technical solution for intelligent monitoring and prevention of runner bean diseases.