When it comes to maintaining a growing population and supporting a healthy economy, the agriculture sector is primary. Infectious plant diseases threaten biodiversity and could result in crop losses. In agriculture, early disease detection and identification from leaf photos using machine learning is a tough but crucial topic of study. Since agriculture is a significant economic factor in India (17% of GDP), there is an urgent need for studies of this nature to be conducted there. Profits for farmers and national GDP can both benefit from more efficient and better crop products. This study proposes a novel approach that combines convolutional neural networks with support vector machines to accurately diagnose 10 distinct illnesses associated with holy basil. CNNs with full connectivity, max-pooling, & convolutional layers are used in this model to automatically extract relevant features from input images. SVMs use these characteristics to make diagnoses. Evaluation measures like as precision, recall, F1-score, and total accuracy reflect the model’s effectiveness. Important in practical agricultural contexts is its capacity to manage data with discrepancies, which is supported by micro-average, macro-average, and weighted-average accuracy ratings.