Detection And Grading of Rust Disease Severities from Wheat Images Using Deep Learning Techniques

Abstract Wheat production, a cornerstone of food security in Ethiopia, is heavily impacted by stripe rust disease, which leads to significant economic losses. Traditional methods for detecting and classifying disease severity are labor-intensive, error-prone, and costly. This study introduces a novel convolutional neural network (CNN)-based model, WRNet, designed for the detection and severity classification of wheat yellow rust disease, along with treatment recommendations. Utilizing 20,000 annotated images collected from Ethiopia, the model applies advanced preprocessing techniques such as noise removal and segmentation using bilateral filtering and k-means algorithms. The WRNet model achieved superior performance with 99.11% training accuracy, 99.04% validation accuracy, and 99% testing accuracy, surpassing pre-trained models such as InceptionV3, InceptionResNetV2, and MobileNetV2. Additionally, the system provides fungicide dosage recommendations tailored to severity levels, ensuring effective disease management. A user-friendly prototype interface developed using Flask enables domain experts to classify disease severity and receive treatment recommendations, offering a scalable solution for precision agriculture in Ethiopia and beyond.