A lightweight rotating target detection method for rice leaf blast based on improved YOLOv8n

Rice leaf blast significantly threatens rice quality and yield, necessitating efficient and precise identification methods for effective field management. Current methods face challenges in accurately detecting leaf blast and distinguishing dense targets due to their small size, scale variation, and dense distribution. This paper proposes a lightweight rotational rice leaf blast detection algorithm named Ro-YOLOv8-PKI. The algorithm adopts Oriented Bounding Boxes (OBB) over traditional Horizontal Bounding Boxes (HBB), uses Gaussian transform for target localization, and replaces ProbIoU with CIoU loss function to improve the accuracy of detecting rotated targets. To achieve model lightweight and improve detection performance to small targets, we replace the 32-fold downsampling-based feature fusion network with a 16-fold downsampling multi-scale feature fusion network. An improved C2f-PKI module is introduced to enhance multi-scale feature extraction and increase the model’s perception of critical regions and attention to central features. Experimental results show that Ro-YOLOv8-PKI outperforms the YOLOv8n baseline, improving F1 score and mean Average Precision (mAP) by 5.8 % and 9.6 %, respectively, while reducing parameters and model size by 69.1 % and 62.7 %. Additionally, the model achieves mAP gains of 2.3 %, 2.2 %, and 3.1 % over other rotated target detection algorithms, including ROI-Transformer, ReDet, and S2-Anet. This approach offers a practical reference for lightweight rice disease detection in natural environments and presents a new perspective on traditional parallel bounding box-based detection methods. An application has also been developed to demonstrate the real-world applicability of Ro-YOLOv8-PKI in field conditions. Part of the rice blast test dataset used in this study and the sheath blight dataset for future research are available at: https://github.com/qingyun259/RiceLeafBlastDataset.