The increasing vulnerability of wheat crops to diseases and pests demands innovative, scalable solutions for timely and accurate crop protection. This study explores the transformative role of deep learning technologies in enhancing disease detection, severity estimation, and overall crop health monitoring for wheat. Thirteen deep learning models, including EfficientNet variants (B0–B7) and state-of-the-art CNN architectures such as VGG19, ResNet152, and InceptionV3, were evaluated on the WheatRust21 and WheatSev datasets. EfficientNetB4 consistently outperformed other models, achieving the highest testing accuracies—98.93% on the original dataset and 99.35% on the augmented dataset—while maintaining computational efficiency. Furthermore, the integration of the Convolutional Block Attention Module (CBAM) with EfficientNetB0 significantly improved the model’s ability to localize and assess wheat rust severity, achieving a testing accuracy of 96.68% on the WheatSev dataset. The research culminated in the development of a mobile application for real-time, in-field diagnosis and severity classification, thereby enhancing accessibility for farmers and extension workers. By combining attention-enhanced deep learning models with practical mobile tools, this work addresses the “last mile” challenge in agricultural AI adoption. It emphasizes the importance of high-quality datasets, edge computing, and explainable AI for deploying robust, scalable, and environmentally sustainable crop protection systems. The findings underscore the potential of AI-powered approaches to significantly contribute to global food security through intelligent, data-driven crop management.