SPW-YOLOv8: A High-Precision and Efficient Deep Learning Model for Rice Leaf Disease Detection

To solve the problems of leakage and low detection accuracy in the detection of different types of diseases in rice leaves, this paper proposes the SPW YOLOv8 (SPD-Conv-RPSA-WIoU-YOLOv8) detection algorithm, which is built based on the YOLOv8 structure and synergistically optimizes the feature extraction, attention, and loss design to improve the detection effect and generalization ability. First, a Sparse Depthwise Convo-lution (SPD-Conv) is used to replace the original convolution to enhance the extraction of low-resolution and small-target features, better adapting to the needs of small-scale and overlapping scene detection of leaves; Then, the RPSA attention module is proposed to optimize the design for the potential correlation between the channel and the spatial features, and the ReLU activation function is employed to introduce a learnable scaling factor to improve the efficiency of the resource allocation and significantly enhance the ability to detect complex backgrounds and tiny targets. Finally, the WIoU v3 loss function is adopted to optimize the detection convergence process and enhance detection accuracy, thereby improving the detection effect. Experiments demonstrate that SPW YOLOv8 achieves 90.2% mAP, 88.0% recall, and 90.2% detection accuracy in the task of detecting rice leaf diseases, which are 2.0%, 5.9%, and 3.7% higher than YOLOv8, respectively. It performs better relative to YOLOv5, YOLO-World, YOLOv11, RT-DETR, and SSD detection algorithms, showing higher detection capability and effectiveness, highlighting the significance of the SPW-YOLOv8 model in improving the detection precision and effectiveness in real agricultural scenarios.