Study on lightweight rice blast detection method based on improved YOLOv8.

Background: Rice diseases that are not detected in a timely manner may trigger large-scale yield reduction and bring significant economic losses to farmers.

Aims: In order to solve the problems of insufficient rice disease detection accuracy and a model that is lightweight, this study proposes a lightweight rice disease detection method based on the improved YOLOv8. The method incorporates a full-dimensional dynamic convolution (ODConv) module to enhance the feature extraction capability and improve the robustness of the model, while a dynamic non-monotonic focusing mechanism, WIoU (weighted interpolation of sequential evidence for intersection over union), is employed to optimize the bounding box loss function for faster convergence and improved detection performance. In addition, the use of a high-resolution detector head improves the small target detection capability and reduces the network parameters by removing redundant layers.

Results: Experimental results show a 66.6% reduction in parameters and a 61.9% reduction in model size compared to the YOLOv8n baseline. The model outperforms Faster R-CNN, YOLOv5s, YOLOv6n, YOLOv7-tiny, and YOLOv8n by 29.2%, 3.8%, 5.2%, 5.7%, and 5.2%, respectively, in terms of the mean average precision (mAP), which shows a significant improvement in the detection performance.

Conclusion: The YOLOv8-OW model provides a more effective solution, which is suitable for deployment on resource-limited mobile devices, to provide real-time and accurate disease detection support for farmers and further promotes the development of precision agriculture. © 2025 Society of Chemical Industry.