The role of big data and machine learning in soil survey and land use planning for sustainable agriculture

Abstract

Soil survey and land use planning are central to sustainable agriculture, yet conventional approaches remain constrained by high costs, limited scalability, and static outputs. The emergence of big data and machine learning (ML) offers new opportunities to enhance soil and land management through improved accuracy, predictive analytics, and dynamic monitoring. Relevant peer-reviewed articles, reports, and book chapters were retrieved from major scientific databases including Scopus, Web of Science, ScienceDirect, and Google Scholar. Additional grey literature, such as institutional reports from FAO, Africa Soil Information Service (AfSIS), and the Global Soil Partnership, was also consulted to capture practical applications in developing regions. This review examines the role of big data and ML in transforming soil survey and land use planning, with emphasis on applications for sustainable agriculture. It highlights the integration of remote sensing, GIS, sensor networks, and heterogeneous datasets for soil fertility prediction, land cover change detection, and land degradation monitoring. Case studies from Africa and other regions illustrate successful applications of supervised, unsupervised, and deep learning techniques in soil classification, crop suitability mapping, and decision support systems. The review further outlines the benefits of these innovations, including enhanced efficiency, real-time monitoring, and evidence-based policymaking, while acknowledging challenges such as data scarcity, infrastructure limitations, technical expertise gaps, and ethical concerns. Future directions emphasize the need for open-access datasets, localized ML models, participatory approaches, and the integration of AI, IoT, drones, and satellite technologies. Big data and ML therefore, represent transformative tools with the potential to advance food security, environmental sustainability, and climate resilience in agricultural systems worldwide.