AI-driven crop disease prediction using satellite imagery and deep learning models

Artificial Intelligence (AI) and satellite imaging are transforming agriculture by enabling precise, data-driven crop health monitoring. This paper explores how deep learning techniques, combined with high-resolution multispectral and hyperspectral satellite imagery, can be leveraged for real-time crop disease detection. By utilizing models like Convolutional Neural Networks (CNNs), YOLO V8, and Faster R CNN, researchers have achieved classification accuracies ranging from 75% to over 99% across crops such as wheat, potatoes, soybean, and bananas. These AI-powered systems empower farmers with early warnings, allowing for timely intervention, reduced pesticide use, and optimized resource management. The integration of satellite data helps in covering large-scale agricultural fields while reducing manual efforts. However, challenges such as atmospheric interference in satellite data, limited labelled datasets, and the need for cross-domain collaboration remain significant. This study presents a system framework and outlines the data acquisition, processing, and evaluation steps necessary for implementing AI-satellite solutions in agriculture. It concludes that combining AI with remote sensing technologies can significantly improve crop disease management and support sustainable farming practices. Future research directions include federated learning, improved sensor integration, and standardized datasets to scale the technology for broader adoption in smart agriculture.