The integration of advanced deep learning models into precision agriculture has the potential to significantly enhance crop health monitoring and disease management. This research explores the application of Vision Transformer (ViT)-based systems for the detection and monitoring of crop diseases, addressing the limitations of conventional Convolutional Neural Networks (CNNs) in capturing long-range dependencies and global contextual information. We propose a ViT-driven framework that leverages highresolution aerial and ground-level imagery to accurately identify a wide range of plant diseases across multiple crop types. The system is trained and evaluated on benchmark agricultural datasets and fieldcollected images, demonstrating superior performance in classification accuracy, robustness to image variability, and early-stage disease detection compared to traditional CNN architectures. Additionally, we incorporate an attention-based interpretability module to provide visual explanations, aiding agronomists in decision-making processes. Our findings highlight the potential of ViT-based models in transforming agricultural practices by enabling scalable, real-time crop monitoring and proactive disease management, thereby contributing to sustainable farming and food security.