Pyramid-YOLOv8: a detection algorithm for precise detection of rice leaf blast

• Published in: Plant methods Vol. 20; no. 1; p. 149
• Main Authors: Cao, Qiang, Zhao, Dongxue, Li, Jinpeng, Li, JinXuan, Li, Guangming, Feng, Shuai, Xu, Tongyu
• Format: Journal Article
• Language: English
• Published: London BioMed Central 28.09.2024; BioMed Central Ltd; BMC
• Subjects: Algorithms; Analysis; Biological Techniques; Biomedical and Life Sciences; blast disease; Computer vision; Disease detection; Explosions; head; Identification and classification; leaves; Life Sciences; Machine vision; Medical research; Medicine, Experimental; Plant Sciences; Pyramid-YOLOv8; Rice; Rice blast disease; Rice leaf blast; Small target; sustainable agriculture; United States; Computer vision; Pyramid-YOLOv8; Small target; Disease detection; Rice leaf blast
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-024-01275-3

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.