Plant Leaf Disease Detection Using Convolution Neutral Network

Abstract

Plant diseases threaten global food security and agricultural productivity. Early and accurate detection of these diseases is crucial for effective disease management and mitigation. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools for image analysis and pattern recognition. This study presents a plant disease prediction framework based on CNNs that can effectively identify and classify diseases in plant leaves. The proposed model leverages a large dataset of labelled plant images and employs transfer learning techniques to enhance its predictive capabilities. Experimental results demonstrate the effectiveness of the CNN-based approach, achieving high accuracy rates in disease identification across multiple plant species. The developed framework holds great promise for assisting farmers and agronomists in making timely and informed decisions for disease management, leading to improved crop health and increased agricultural productivity.