Geospatial big data analytics for Precision Agriculture: Enhancing Productivity and Sustainability

Geospatial big data analytics is rapidly reshaping the landscape of precision agriculture by enabling more informed, efficient, and sustainable farming practices. This review critically examines the convergence of geospatial technologies such as remote sensing, Geographic Information Systems (GIS), and unmanned aerial systems with big data analytics techniques, including machine learning, predictive modeling, and cloud computing. Key applications explored include crop health monitoring, site-specific soil management, climate and weather analytics, and sustainability assessment, all of which support data-driven decision-making across the agricultural value chain. The paper synthesizes recent developments from 2022 to 2025, highlighting how these innovations are enhancing yield prediction, resource optimization, and environmental stewardship. It also addresses prevailing challenges related to data integration, interoperability, infrastructure scalability, data privacy, and user adoption. In response, the review outlines emerging research priorities, including the deployment of edge computing for real-time analytics, the integration of artificial intelligence and blockchain for secure and transparent data ecosystems, and the advancement of climate-resilient and socially inclusive agricultural models. Collectively, these directions are vital for achieving global food security and promoting sustainable agricultural systems in an era of climatic and demographic uncertainty.