Leaf Disease Detection by Convolutional Neural Network (CNN)

Abstract

Effective plant disease detection is paramount for ensuring global food security and sustainable agricultural practices. This article presents a comprehensive exploration of utilizing machine learning, specifically Convolutional Neural Networks (CNNs), for accurate and timely plant disease classification. The research delves into the integration of diverse datasets, preprocessing techniques, and CNN architecture to create a robust disease detection model. The dataset, encompassing various crops and diseases, forms the foundation for training the model, while preprocessing ensures optimal data quality. The CNN architecture, meticulously designed, progresses through convolutional and pooling layers to extract hierarchical features from input images. The trained model achieves an accuracy of 92.23% on disease classification, showcasing the potential of such technology in agricultural innovation. The article underscores the pivotal role of machine learning in revolutionizing plant disease detection, thus contributing to global food security and sustainable agriculture.