Plant diseases pose significant challenges to agricultural productivity and crop health. In this project, we develop a system for automated plant disease detection using Convolutional Neural Networks CNNs. The goal is to assist farmers and plant enthusiasts in early disease identification and provide targeted supplement recommendations for effective treatment. The project leverages the power of deep learning and computer vision techniques to analyze plant images and detect signs of diseases. A diverse dataset of plant images, encompassing various healthy and diseased states, is used to train the CNN model. The images are pre-processed, resized, and fed into the network, which employs multiple convolutional and pooling layers to learn hierarchical and discriminative features. The project also encompasses the development of a user-friendly web- based interface using the Flask framework. Users can conveniently upload plant images through the interface and receive prompt disease detection results along with personalized supplement recommendations. The system aims to empower farmers, horticulturists, and plant caregivers with a reliable tool to enhance disease management and optimize plant health.

Plant Disease Detection, Convolutional Neural Network, Deep Learning, Deep Learning, Computer Vision, Supplement Recommendation, Agricultural Productivity, Disease Management