Predictive Model for Crop Disease and Management System Using Machine Learning with Deployment Through Flask

Crop diseases have a disproportionately large economic effect on farmers and threaten food security. Predictive Model for Crop Disease and Management System, which uses machine and deep learning to automate disease identification and management is this project. Consuming the VGG19 pre trained model for multi class classification, the system is able to distinguish between crop diseases based on leaf images at high accuracy. Users can upload crop images, receive real time disease predictions and obtain actionable management recommendations, including use of pesticides, with a web application that is on top of Flask. By integrating data augmentation techniques for model generalization, a robust knowledge base for disease management, and feedback mechanisms for continuous refinement, the proposed system constitutes an amalgam of many of the above techniques. This system bridges this gap between the latest technologies and what is truly practical farming needs, and enables early detection of diseases, sustainable agriculture and effective disease management. Future additions are to expand the disease database, include real time field data, and multiple languages for better accessibility.