Plant diseases pose a major threat to agricultural productivity and economies dependent on it. Monitoring plant growth and phenotypes is vital for early disease detection. In Indian agriculture, black-gram (Vigna mungo) is an important pulse crop afflicted by viral infections like Urdbean Leaf Crinkle Virus (ULCV), causing stunted growth and crinkled leaves. Such viral epidemics lead to massive crop losses and financial distress for farmers. According to the FAO, plant diseases cost countries $220 billion annually. Hence, there is a need for quick and accurate diagnosis of crop diseases like ULCV. Recent advances in computer vision and image processing provide promising techniques for automated non-invasive disease detection using leaf images. The key steps involve image pre-processing, segmentation, informative feature extraction, and training machine learning models for reliable classification. In this work, an automated ULCV detection system is developed using black gram leaf images. The Grey Level Co-occurrence Matrix (GLCM) technique extracts discriminative features from leaves. Subsequently, a deep convolutional neural network called YOLO (You Only Look Once) is leveraged to accurately diagnose ULCV based on the extracted features. Extensive experiments demonstrate the effectiveness of the GLCM-YOLO pipeline in identifying ULCV-infected leaves with high precision. Such automated diagnosis can aid farmers by providing early disease alerts, thereby reducing crop losses due to viral epidemics.