Abstract: Rice leaf disease prevention plays an important role in improving rice yield. Aiming at the problems of slow manual inspection speed and high subjectivity of rice leaf disease, a target detection method of rice leaf disease based on improved Yolov5s is proposed. The K-means clustering algorithm is used to obtain the prior frame size, which enhances the adaptability of the detection model to rice leaf disease. The lightweight spatial attention and channel attention are fused to enhance the high-level semantic feature information and the model’s awareness of disease information. Finally, the feature pyramid network is combined with the multi-scale receptive field to obtain target context information, which effectively enhances the model’s extraction of features around the target and improves the accuracy of target detection. The experimental results show that the average detection accuracy（IOU=0.5） of the improved Yolov5s algorithm is increased by 4.3%, the F1 value is increased by 5.3%, and the FPS is 58.7 f/s. The proposed method effectively improves the detection accuracy of the Yolov5s algorithm for rice leaf disease and meets the demand of real-time detection.