Plant Leaf Disease Detection System

Abstract

Plant diseases pose a significant threat to agricultural productivity and food security. Early and accurate detection of leaf diseases is essential to minimize crop loss and improve yield quality. This project focuses on detecting plant leaf diseases using deep learning techniques. A CNN model is used to analyze leaf images and identify diseases accurately. The system improves detection speed and helps farmers take timely actions to protect crops.The proposed system processes leaf images through preprocessing steps such as resizing, normalization, and augmentation, followed by feature extraction and classification using a trained cnn model. The model is trained on a publicly available dataset and achieves an accuracy of approximately 92%. The system provides fast and reliable disease identification, making it suitable for real-time agricultural applications. This approach reduces dependency on manual inspection and helps farmers take timely corrective actions.