Early and accurate detection of maize leaf diseases is crucial for preventing yield loss and reducing agrochemical use. Existing vision-based models struggle in realworld agricultural conditions due to data imbalance, lighting variability, and high computational demands. This paper introduces MaizeFormerX, a lightweight Vision Transformer that utilizes multi-scale patch embedding and a Cross-Scale Attention Fusion (CSAF) module. This design enables effective detection of fine-grained lesion textures and broader disease patterns while maintaining a low parameter count. MaizeFormerX was evaluated on two public datasets (Dataverse and Tanzania) using specific preprocessing, targeted augmentation, and stratified 10-fold cross-validation. The model achieved accuracies of 97.8%,97.5%, and 96.9% on the Dataverse, Tanzania, and Plagues Maiz datasets, respectively, outperforming the Swin Transformer V2 by 2−3% with significantly fewer parameters and lower computational costs. Grad-CAM visualizations provide pixel-level interpretability, and this has been implemented in a cost-effective web application for real-time field use. This research offers an accurate, efficient, and explainable framework for diagnosing maize diseases, bridging academic findings with practical agricultural applications, and has the potential to enhance crop management in resource-constrained environments.