Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques

Abstract

The global wheat industry faces significant challenges due to yellow rust disease, This is induced by fungus Puccinia striiformis, as it leads to substantial crop losses and economic impacts. Timely detection and classification of the disease are essential for its effective management and control. In this study, we investigate the potential of DL and ML techniques for detecting and classifying yellow rust disease in wheat. We utilize three state-of-the-art CNN models, namely ResNet50, DenseNet121, and VGG19, to analyze wheat leaf images and extract relevant features. These models were developed and refined using a large dataset of annotated wheat photos. Encompassing both healthy plants and those affected by yellow rust disease. Furthermore, we examine the effectiveness of data augmentation and transfer learning in enhancing classification performance. Our findings reveal that the DL-based CNN models surpass traditional machine learning techniques in detecting and classifying yellow rust disease in wheat. Among the tested CNN models, EfficientNetB3 demonstrates the best performance, emphasizing its suitability for large-scale and real-time monitoring of wheat crops. This research contributes to the development of precision agriculture tools, laying the groundwork for prompt intervention and management of yellow rust disease, ultimately minimizing yield loss and economic impact on wheat production.