PLANT LEAF DISEASE RECOGNITION SYSTEMS

Plant diseases significantly impact agricultural productivity, leading to substantial economic losses and reduced crop quality. Early and accurate detection of leaf diseases is essential for effective crop management and improved yield. This research presents a Plant Leaf Disease Recognition System that leverages image processing and deep learning techniques to automatically identify diseases from leaf images. The proposed system involves preprocessing, segmentation, feature extraction, and classification using a convolutional neural network (CNN). A curated dataset of healthy and diseased leaves is used to train and validate the model. Experimental results demonstrate high accuracy and robustness across multiple crop types and disease categories. The system provides a fast, cost-effective, and scalable approach to disease diagnosis, supporting farmers and agricultural experts in making timely decisions and reducing dependency on manual inspection.