Jiang Qian, Wang Hongli, Sun Zhenyu, Cao Shiqin, Wang Haiguang
College of Plant Protection, China Agricultural University, Beijing 100193, China.
Institute of Plant Protection, Gansu Academy of Agricultural Sciences, Lanzhou 730070, China.
Plants (Basel). 2024 Oct 10;13(20):2835. doi: 10.3390/plants13202835.
Stripe rust caused by f. sp. and leaf rust caused by , are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.
由条形柄锈菌小麦专化型（Puccinia striiformis f. sp. tritici）引起的条锈病和由叶锈菌（Puccinia triticina）引起的叶锈病是小麦上的两种毁灭性病害，严重影响小麦的生产安全。及时检测和识别这两种病害对于采取有效的病害管理措施以减少小麦产量损失至关重要。为了实现小麦条锈病和叶锈病在不同生长阶段的准确识别，本研究基于深度学习并利用图像处理技术，对不同生长阶段小麦条锈病和叶锈病的图像识别进行了研究。基于YOLOv5s模型，我们构建了小麦条锈病和叶锈病在苗期、拔节期、孕穗期、抽穗期、开花期、灌浆期以及所有生长阶段的识别模型。这些模型在不同个体生长阶段以及所有生长阶段的不同测试集上进行了测试。结果表明，这些模型在病害图像识别中的表现各不相同。基于单个生长阶段获取的病害图像构建的模型不适用于识别其他单个生长阶段获取的病害图像，不过基于灌浆期获取的病害图像构建的模型在开花期和灌浆期的测试集上具有可接受的识别性能。此外，结果表明小麦生长阶段对这两种病害的图像识别有很大影响。基于所有生长阶段获取的病害图像构建的模型产生了可接受的识别结果。在由单个生长阶段获取的病害图像组成的每个测试集以及由所有生长阶段获取的病害图像组成的测试集上，平均F1分数值在64.06%至79.98%之间，平均精度均值（mAP）值在66.55%至82.80%之间。本研究为不同生长阶段小麦条锈病和叶锈病的图像识别提供了依据，并为其他植物病害的准确识别提供了参考。
