Cao Qiang, Zhao Dongxue, Li Jinpeng, Li JinXuan, Li Guangming, Feng Shuai, Xu Tongyu
College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, 110866, China.
Liaoning Key Laboratory for Intelligent Agricultural Technology, Shenyang, 110866, China.
Plant Methods. 2024 Sep 28;20(1):149. 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.
稻瘟病是影响水稻产量和品质的主要病害，对其进行有效检测对于确保水稻产量和促进农业可持续生产至关重要。为解决传统病害检测方法耗时且效率低下的问题，本研究提出了一种名为Pyramid-YOLOv8的方法，用于快速、准确地检测水稻叶片稻瘟病。该算法基于YOLOv8x网络框架构建，具有多注意力特征融合网络结构。这种结构增强了原始特征金字塔结构，并与一个额外的检测头协同工作以提高性能。此外，本研究设计了一个轻量级的C2F-Pyramid模块以提高模型的计算效率。在对比实验中，Pyramid-YOLOv8表现出色，平均精度均值（mAP）为84.3%，与Faster-RCNN、RT-DETR、YOLOv3-SPP、YOLOv5x、YOLOv9e和YOLOv10x模型相比，分别提高了9.9%、4.3%、7.4%、6.1%、1.5%、3.7%和8.2%。此外，它的检测速度达到62.5 FPS；该模型仅包含42.0M个参数。同时，模型大小和浮点运算量（FLOPs）分别减少了41.7%和23.8%。这些结果证明了Pyramid-YOLOv8在检测水稻叶片稻瘟病方面的高效性。综上所述，本研究开发的Pyramid-YOLOv8算法为水稻病害检测提供了坚实的理论基础，并为农业生产中的病害管理和预防策略引入了新的视角。
