Classification and Detection of Plant Disease Using CNN and Machine Learning

Abstract

The detection and classification of plant diseases are crucial to ensuring food security and maximizing agricultural productivity. Traditional methods of plant disease identification are time-consuming and labor-intensive, necessitating the adoption of more efficient and accurate techniques. In recent years, advancements in machine learning have led to the development of robust methods for automated plant disease detection. This research paper presents a novel approach for plantdisease detection using Convolutional Neural Networks (CNNs). CNNs have demonstrated exceptional performance in image recognition tasks, making them a promising choice for detecting diseases in plant images. The proposed system utilizes a pre- processed dataset of plant images, comprising both healthy and diseased samples, to train the CNN model. Keywords—Plant disease and detection, convolutional neural networks, deep learning architectures, feature extraction, classifier methods