LeafLense: Advanced Leaf Disease Detection

Computer Science & Engineering

Plant diseases are a critical issue in agriculture, affecting crop health, reducing yield, and threatening global food security. Early and accurate detection of these diseases is essential to prevent their spread and ensure timely treatment. This project presents a deep learning-based approach to detect plant leaf diseases in crops such as potato, bell pepper, corn, beans, and sunflower. A Convolutional Neural Network (CNN) model is trained on a diverse dataset of leaf images to identify and classify diseases with high accuracy. The system eliminates the need for manual inspection, minimizes human error, and offers a faster, more efficient way to support farmers and agricultural experts in disease diagnosis. To make this solution accessible and user-friendly, a web-based application has been developed using Python and the Flask framework. The frontend is built using HTML, CSS, and JavaScript to provide a smooth, responsive user experience, while SQL is used for data storage and management. Users can upload leaf images through the interface, and the system processes the input using the trained model to predict the disease type along with a confidence score. The project ensures security, scalability, and real-time functionality. With minimal hardware requirements, the system is affordable and easy to implement. It can also be extended to mobile platforms or integrated into smart farming systems for field use. This project highlights the effective use of deep learning and modern web technologies to contribute to precision agriculture, helping improve crop health monitoring and promoting sustainable farming practices.

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