What can artificial intelligence do for soil health in agriculture?
Stefan Schweng, Luca Giuliano Bernardini, Katharina Keiblinger, Hans‐Peter Kaul, Iztok Fister, Niko Lukač, Javier Del Ser, Andreas Holzinger
Abstract
The integration of artificial intelligence (AI) into soil research presents significant opportunities to advance the understanding, management, and conservation of soil ecosystems. This paper reviews the diverse applications of AI in soil health assessment, predictive modeling of soil properties, and the development of pedotransfer functions within the context of agriculture, emphasizing AI’s advantages over traditional analytical methods. We identify soil organic matter decline, compaction, and biodiversity loss as the most frequently addressed forms of soil degradation. Strong trends include the creation of digital soil maps, particularly for soil organic carbon and chemical properties using remote sensing or easily measurable proxies, as well as the development of decision support systems for crop rotation planning and IoT-based monitoring of soil health and crop performance. While random forest models dominate, support vector machines and neural networks are also widely applied for soil parameter modeling. Our analysis of datasets reveals clear regional biases, with tropical, arid, mild continental, and polar tundra climates remaining underrepresented despite their agricultural relevance. We also highlight gaps in predictor–response combinations for soil property modeling, pointing to promising research avenues such as estimating heavy metal content from soil mineral nitrogen content, microbial biomass, or earthworm abundance. Finally, we provide practical guidelines on data preparation, feature extraction, and model selection. Overall, this study synthesizes recent advances, identifies methodological limitations, and outlines a roadmap for future research, underscoring AI’s transformative potential in soil science. • AI advances soil health assessment and supports sustainable management strategies. • New predictor–response combinations are key for future soil parameter modeling. • Need for more data being collected regularly using consistent, standardized methods. • Soil data lacks representation from Central/South Asia, Australia, and tundras. • GNNs, PINNs, RL, Multi-task learning & SHAP/LIME remain underused in soil research.