Li Jingtao, Wu Jiawei, Liu Rui, Shu Guofeng, Liu Xia, Zhu Kun, Wang Changyi, Zhu Tong
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650504, China.
College of Plant Protection, Yunnan Agricultural University, Kunming, 650201, China.
Sci Rep. 2024 Dec 28;14(1):31046. doi: 10.1038/s41598-024-82272-3.
Potato late blight is a common disease affecting crops worldwide. To help detect this disease in complex environments, an improved YOLOv5 algorithm is proposed. First, ShuffleNetV2 is used as the backbone network to reduce the number of parameters and computational load, making the model more lightweight. Second, the coordinate attention mechanism is added to reduce missed detection for leaves that are overlapping, damaged, or hidden, thereby increasing detection accuracy under challenging conditions. Lastly, a bidirectional feature pyramid network is employed to fuse feature information of different scales. The study results show a significant improvement in the model's performance. The number of parameters was reduced from 7.02 to 3.87 M, and the floating point operations dropped from 15.94 to 8.4 G. These reductions make the model lighter and more efficient. The detection speed increased by 16 %, enabling faster detection of potato late blight leaves. Additionally, the average precision improved by 3.22 %, indicating better detection accuracy. Overall, the improved model provides a robust solution for detecting potato late blight in complex environments. The study's findings can be useful for applications and further research in controlling potato late blight in similar environments.
马铃薯晚疫病是一种影响全球农作物的常见病害。为了帮助在复杂环境中检测这种病害，提出了一种改进的YOLOv5算法。首先，使用ShuffleNetV2作为骨干网络来减少参数数量和计算量，使模型更轻量化。其次，添加坐标注意力机制以减少对重叠、受损或隐藏叶片的漏检，从而在具有挑战性的条件下提高检测精度。最后，采用双向特征金字塔网络来融合不同尺度的特征信息。研究结果表明该模型的性能有显著提升。参数数量从7.02 M减少到3.87 M，浮点运算从15.94 G降至8.4 G。这些减少使得模型更轻量、更高效。检测速度提高了16%，能够更快地检测马铃薯晚疫病叶片。此外，平均精度提高了3.22%，表明检测精度更高。总体而言，改进后的模型为在复杂环境中检测马铃薯晚疫病提供了一个强大的解决方案。该研究结果对于在类似环境中控制马铃薯晚疫病的应用和进一步研究可能有用。
