Abstract:An improved YOLOv8 model (FCU-YOLOv8) is proposed to enhance the accuracy and efficiency of rice disease detection, addressing the challenges of diverse rice diseases, complex backgrounds, and subtle differences in characteristics between diseases. The FasterNeXt module is used to replace the C2f module in the YOLOv8 backbone network. By optimizing the network structure, the FasterNeXt module reduces computation and memory access while improving feature extraction efficiency, thus lowering the inference cost of the model. The C3K module (multi-scale convolution module) and CPSA module (convolutional attention mechanism) are designed to further enhance the model’s ability to perceive disease region features. The C3K module allows the model to adapt to disease characteristics at various scales through flexible convolutional kernel selection, while the CPSA module employs an attention mechanism to enhance the model’s ability to capture key information. To improve the quality of detection boxes and the detection performance of dense disease targets, the optimized unified intersection over union (UIoU) loss function is adopted. This function improves detection performance by balancing the accuracy and consistency of bounding boxes during the regression phase. On a custom-made image dataset of eight common rice diseases, FCU-YOLOv8 demonstrates significant improvements over the original YOLOv8 in several performance metrics. The mAP@0.5 index reaches 94.7%, a 2.4% improvement over the baseline model, and the mAP@0.5:0.95 index reaches 67.2%, a 3.3% improvement. The model’s parameters are reduced by 24.2%, and the calculated floating-point operations decrease by 28.7%, compared to the baseline model in terms of model lightweighting. Compared with mainstream algorithms, the proposed algorithm outperforms current leading algorithms, demonstrating the effectiveness of the network.