Crop Disease Prediction Using Deep Learning Algorithm - A Review

Crop diseases pose a significant threat to global food security, leading to substantial economic losses and reduced agricultural productivity. Traditional disease detection methods, which rely on manual inspection and chemical tests, are often labour-intensive, time-consuming, and prone to inaccuracies. In recent years, deep learning has emerged as a powerful tool for automating crop disease detection and prediction. Convolutional Neural Networks (CNNs) and other advanced architectures have demonstrated high accuracy in identifying plant diseases using image-based analysis. This paper provides a comprehensive review of deep learning approaches in crop disease prediction, discussing key datasets, preprocessing techniques, model architectures, challenges, and future directions. Despite the advancements, challenges such as data scarcity, model generalization, and computational limitations remain. Addressing these issues through improved dataset diversity, explainable AI, and efficient deep learning models can further enhance the reliability and applicability of these technologies in precision agriculture. By integrating deep learning into modern farming practices, the agricultural industry can benefit from timely disease detection, reduced crop losses, and improved food security.