Agriculture is under serious threat, and this threat includes diseases that affect plant leaves. Our method pinpoints both the disease that affected the leaf and the region of damage. Both the quantity and quality of agricultural products are impacted by crop diseases, particularly those that primarily harm the leaves. The human eye\'s ability to see subtle differences in the diseased leaf area is not as strong as it should be. In this study, we provide an automated web-based system for classifying and diagnosing plant leaf diseases. We are using CNN model as a feature extractor and prediction model to swiftly categorise illnesses. Proper treatment can be provided by the study of disease. Along with this we have utilized the docker, POSTMAN API and TF serving server to make the system scalable and improve the working. This study is validated using the Plant Village dataset for plants like tomato and potato. The training and testing results indicate that the CNN model have a greater classification accuracy than the currently in use ANN model. The proposed approach could prove a useful tool for farmers and industry specialists to utilise when making decisions about crop management and disease control.