Remote Sensing and Agroinformatics Insights in Saudi Arabia Using Machine Learning

Overview

Agriculture is a cornerstone of Saudi Arabia’s national strategy, playing a critical role in food security. However, this sector is also the primary driver of water demand, accounting for over 80% of the Kingdom's total water resources, primarily sourced from non-renewable groundwater. This talk explores how machine learning and high-resolution satellite remote sensing are being used to transform vast amounts of raw data into actionable agroinformatics at a national scale, providing the precision needed to manage these vital resources sustainably. The presentation provides the first-ever comprehensive reports on several key agricultural dynamics within the Kingdom. This includes a 30-year retrospective analysis of center-pivot irrigation fields and the specialized mapping of date palm plantations. These data insights offer a unique, long-term view of agricultural expansion and contraction, demonstrating how machine learning bridges the gap between satellite imagery and real-world agricultural management. Ultimately, these findings provide a scalable blueprint for optimizing water use and ensuring the long-term sustainability of agricultural systems in arid environments.