Yellow rust disease caused by Puccinia striiformis f. sp. tritici, a pathogen in wheat, results in significant losses in wheat production worldwide due to its high destructive property. On the other side, yellow rust can be taken under control by growing resistant cultivars, by the application of fungicides, and by the use of appropriate cultural practices. Thus, it is crucial to detect the disease at an early stage. The current study offers to use computerized models in determining the infection type of yellow rust disease in wheat. Herein, a deep convolutional neural networks-based model, named Yellow-Rust-Xception, was proposed. The model inputs the wheat leaf image and classifies it as no disease, resistant, moderately resistant, moderately susceptible, or susceptible according to the rust severity, i.e. percentage. The convolutional neural networks, a state-of-art approach, have layered structures those inspired by the human brain and able to learn discriminative features from data automatically; thus networks performance match and even surpass humans in task-specific applications, a newly developed dataset containing yellow rust-infected wheat leaf images, was used to train, validate, and test Yellow-Rust-Xception, in result, the test accuracy was 91%. Thus, Yellow-Rust-Xception can be used in determining wheat yellow rust and its severity level.

引起的黄锈病条锈菌F。sp. 小麦小麦中的病原体，由于其高破坏性，导致全世界小麦生产的重大损失。另一方面，黄锈病可以通过种植抗病品种、使用杀菌剂和使用适当的栽培方法来控制。因此，早期发现疾病至关重要。目前的研究提出使用计算机模型来确定小麦黄锈病的感染类型。在此，提出了一种基于深度卷积神经网络的模型，名为 Yellow-Rust-Xception。模型输入小麦叶片图像，根据锈病的严重程度，即百分比，将其分类为无病、抗病、中抗、中感或易感。卷积神经网络，一种最先进的方法，具有受人脑启发的分层结构，能够自动从数据中学习判别特征；因此，网络性能在特定任务应用中匹配甚至超过人类，使用新开发的包含黄锈病感染小麦叶片图像的数据集来训练、验证和测试 Yellow-Rust-Xception，结果，测试准确率为 91 %。因此，Yellow-Rust-Xception 可用于确定小麦黄锈病及其严重程度。