A Novel Wheat Leaf Disease Classifier Leveraging Generative Adversarial Networks - Saunshi, Girish; Badiger, Ramesh; Yakkundimath, Rajesh; Chini, Shridhar (International Journal of Computer Sciences and Engineering, 2025)
Automatic diagnosis and control of wheat plant disease are highly desired by agricultural experts. Accurate diagnosis of wheat leaf diseases is important for effective crop management. This study introduces a Wheat Leaf Convolutional (WLC) model, an enhancement of the VGG16 architecture, designed to detect and classify six distinct types of wheat leaf diseases using deep learning techniques. The model is trained using wheat leaf images dataset, augmented by Generative Adversarial Networks (GANs) to improve generalization. The WLC model got an accuracy of 94.88%, outperforming classical CNN models such as ResNet-50, AlexNet, and MobileNet by significant margins. Key metrics, including recall, precesion and F1-score, were evaluated across six disease categories: Leaf Rust, Black Chaff, Powdery Mildew, Wheat Streak, Septoria, and Healthy plants. Experimental results show that the WLC model accurately and efficiently identifies diseases, making it a useful tool for real- time applications in precision agriculture. This work contributes to improving wheat disease diagnosis, enabling timely interventions and better crop management practices.