The early and accurate detection of foliar diseases is crucial for improving crop yield and ensuring sustainable agricultural practices. This study presents a Plant Foliar Disease Identification Model (PFDIM) for mung bean (Vigna radiata) leaves using a Support Vector Machine (SVM) classifier, specifically designed for operation in uncontrolled environmental conditions. A custom dataset of 1,307 field-captured images was developed, encompassing both healthy and diseased samples affected by Cercospora Leaf Spot, Powdery Mildew, and Yellow Mosaic Virus. The proposed model integrates preprocessing techniques such as image resizing, augmentation, HSV-based segmentation, and Histogram of Oriented Gradients (HOG) feature extraction to enhance robustness and accuracy. Through Grid Search optimization with 5-fold cross-validation, the model achieved 100% training accuracy and 89% testing accuracy, demonstrating strong generalization across variable lighting, background, and noise conditions. Furthermore, a user-friendly interface, titled Leaf Disease Classification, was developed to allow real-time detection and classification through simple image uploads, enabling accessibility for farmers and researchers. The results confirm that SVM provides reliable, scalable, and cost-effective performance for disease identification in real-world agricultural environments. This research contributes a practical, AI-based solution for early mung bean disease detection, supporting precision agriculture and sustainable crop management practices.