Rice diseases such as brown spot, white leaf blight, bacterial blight and rice blast occur frequently during rice development stages, causing serious losses in rice production. Aiming at the challenges in accurately identifying rice diseases under natural conditions, where background is complex, and differences between disease classes are subtle, a rice disease detection network model (RDN-YOLO) was proposed to improve the accuracy of rice disease detection. Firstly, the YOLO v5 network was used as the basic framework, and the C2f module was embedded in the backbone network to enhance the model’s perception of disease features. Besides, the SPDConv was introduced in the backbone network to expand the model’s perception field and further improve the feature extraction ability of minor disease spots. Secondly, the SPDConv was embedded in the neck network, and the lightweight convolution GsConv was used to replace the standard convolution, which can improve the accuracy of positioning of the disease site and prediction of category information and inference speed, contributing to higher accuracy. The model was trained and tested on a dataset comprising images of five common rice diseases: ear blast, leaf blast, leaf spot, smut, and bacterial blight, where the dataset were collected under natural environment. Experimental results showed that the proposed model achieved a disease detection accuracy of 94.2% with mAP of 93.5% and model parameters of 8.1MB. Compared with other models YOLO v5, Faster R-CNN, YOLO v7 and YOLO v8, the complexity of the proposed model was only slightly lower than that of YOLO v5, but the mAP was approximately 12.2 percentage points than that of YOLO v5, which signified a notable advancement in rice disease detection, achieving high accuracy while reducing model complexity to a certain extent.