The correct identification and management of leaf diseases are essential for the health of the crop and for productivity in agriculture. This need is addressed in this project by integrating advanced deep learning image processing and web development to create an innovative solution. This is a web-based platform designed for the early identification and classification of leaf diseases, providing farmers with accessible tools to effectively monitor and maintain the crop health. It uses a modified version of the LeNet architecture, trained to the specific characteristics of the data set, to upload leaf images, analyze them, and accurately detect disease symptoms and suggest the required treatment. The platform has a user-friendly interface that accommodates users with different technical expertise, providing seamless image uploads and providing actionable insights in real time. By providing precise and timely disease detection, the system helps to prevent high crop damage. This targeted approach improves disease management, reduces crop losses, and optimizes the utilization of resources while promoting sustainable farming practices by reducing the reliance on different kinds of solutions. Beyond the quick diagnosis, the solution offers valuable educational resources on disease prevention and management, empowering valuable knowledge to farmers. Data privacy and authorized access are ensured with a secured backend. Future replications aim to expand the platform into an understanding application with additional features and further advance agricultural methods by preventing diseases and nurturing healthy crop growth.