SerpensGate-YOLOv8: an enhanced YOLOv8 model for accurate plant disease detection
Yujie Miao, Meng Wei, Xiaoyu Zhou
Abstract
Plant disease detection remains a significant challenge, necessitating innovative approaches to enhance detection efficiency and accuracy. This study proposes an improved YOLOv8 model, SerpensGate-YOLOv8, specifically designed for plant disease detection tasks. Key enhancements include the incorporation of Dynamic Snake Convolution (DySnakeConv) into the C2F module, which improves the detection of intricate features in complex structures, and the integration of the SPPELAN module, combining Spatial Pyramid Pooling (SPP) and Efficient Local Aggregation Network (ELAN) for superior feature extraction and fusion. Additionally, an innovative Super Token Attention (STA) mechanism was introduced to strengthen global feature modeling during the early stages of the network. The model leverages the PlantDoc dataset, a highly generalizable dataset containing 2,598 images across 13 plant species and 27 classes (17 diseases and 10 healthy categories). With these improvements, the model achieved a Precision of 0.719. Compared to the original YOLOv8, the mean Average Precision ([email protected]) improved by 3.3%, demonstrating significant performance gains. The results indicate that SerpensGate-YOLOv8 is a reliable and efficient solution for plant disease detection in real-world agricultural environments.