Predictive Medicine Model For Plant Wellness Assessment

Agriculture is the backbone of many economies, providing food and raw materials essential for human survival. However, plant diseases significantly impact crop productivity, leading to financial losses and food shortages. Early detection and appropriate treatment of plant diseases are critical to maintaining agricultural sustainability. This project, "Plant Disease Detection and Fertilizer Suggestion," presents an AI-powered system that identifies plant diseases using deep learning techniques and suggests suitable fertilizers based on the detected diseases. Traditional plant disease detection methods rely on manual inspection, which is time consuming, prone to human error, and requires expert knowledge. With advancements in computer vision and artificial intelligence, automated systems can now accurately classify plant diseases and provide recommendations. Our system utilizes Convolutional Neural Networks (CNNs) to analyze plant leaf images and detect diseases with high accuracy. By leveraging deep learning, the model can differentiate between multiple plant diseases, ensuring precise diagnosis.