Machine Learning Framework for Early Detection of Crop Disease

Abstract

This review paper looks at recent advancements in crop disease detection through deep learning techniques. Crop diseases significantly lowers agricultural productivity, and accurate diagnosis is essential for effective disease management. In order to identify crop illnesses, the study offers a thorough examination of a number of deep learning models, such as hybrid architectures, recurrent neural networks (RNNs), and convolutional neural networks (CNNs). The prospects, challenges, and future directions of incorporating deep learning for precise and quick crop disease identification are also explained in this research. The insights provided offer hope for the development of sustainable agricultural practices through the application of cutting-edge technologies in disease diagnosis and management.