ArticlePlant methods2024
Pyramid-YOLOv8: a detection algorithm for precise detection of rice leaf blast.
Article in Plant methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Identification of CAMTA transcription factors and functional analysis of OsCAMTA4 in rice blast and salt stress.Planta · 2026Article
- A Novel Ensemble Learning Approach for Grouping the State-of-the-Art YOLOV10 and YOLOV11 Models for Kidney Stone Detection in CT and Ultrasound Images.Journal of imaging informatics in medicine · 2026Article
- CED-RTDETR: a contour-aware evidence-guided decoupled network for rice leaf disease detection.Frontiers in plant science · 2026Article
- ADAM-DETR: an intelligent rice disease detection method based on adaptive multi-scale feature fusion.Plant methods · 2025Article
- A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques.Scientific reports · 2025Article
- YOLO-ODD: an improved YOLOv8s model for onion foliar disease detection.Frontiers in plant science · 2025Article
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7 authors.
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Abstract
Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods' time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model's computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.
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