• Achieved 98.33% validation accuracy through extensive data augmentation and preprocessing techniques tailored to enhance model performance in wheat disease classification • Conducted a comparative evaluation of advanced deep learning architectures, including ResNet, EfficientNet, VGG, Inception, MobileNet, and DenseNet, to identify the most effective model for accurately classifying wheat rust diseases. • The developed model outperforms existing architectures, demonstrating notable improvements in precision, recall, and F1 scores critical for real-world applications.