LeafLens: Detect Plant Disease Before They Spread

LeafLens is an intelligent plant disease detection system designed to assist farmers and agriculturists in identifying plant diseases efficiently and accurately. Leveraging the power of deep learning, the system employs a Convolutional Neural Network (CNN) architecture trained on a comprehensive dataset of diseased and healthy leaf images. The application streamlines the diagnosis process by analyzing images of plant leaves and providing precise predictions about the disease type. The backend is developed using Python and PyTorch, ensuring robust model performance and scalability. The system integrates image preprocessing techniques such as resizing, cropping, and normalization to ensure uniform input to the model. The frontend is designed to be user-friendly, allowing users to upload leaf images seamlessly. Real-time inference capabilities provide instant feedback, displaying the disease classification alongside recommendations for treatment. LeafLens also incorporates features like data visualization for training and validation losses, accuracy tracking, and detailed performance metrics, enabling continuous improvement of the model. The system's framework supports scalability, allowing integration into mobile and web platforms for broader accessibility. By offering a reliable, efficient, and cost-effective solution, LeafLens aims to revolutionize agricultural disease management, promoting healthier crops and sustainable farming practices.