Abstract: In order to address the problem that traditional deep learning algorithms are difficult to identify rice diseases accurately and efficiently in complex environments, an improved YOLOv5 algorithm was proposed to detect the disease spots of common rice blight, rice blast, tungro disease and brown spot disease. A hybrid domain attention mechanism was combined with the original YOLOv5 algorithm for feature correction, which improved the model′s ability to determine the position of rice leaves and disease spot location. The original CIoU＿loss（Complete Intersection over Union） was replaced by SIoU＿loss（SCYLLA Intersection over Union） in the loss function part to compensate for the problem that CIoU＿loss did not focus on the angular offset of the bounding box and the Ground truth box. Soft-NMS（Soft Non Maximum Suppression） was chosen to filter the prediction boxes to alleviate the leakage caused by the overlapping area of different lesions in the conventional NMS. In the ablation experiment, the mAP（mean Average Presicion） of the improved algorithm reached 0. 884 in the rice disease identification task, which was 2. 9 percentage points higher than the original YOLOv5 algorithm, and the improvement was greater in the identification of brown spot. It proved the effectiveness of the improved YOLOv5 algorithm in the rice disease identification task.