Artificial intelligence in soil microbiome-driven agriculture: from practical limits to a translational roadmap

Abstract

Background Soil microbiome research has been revolutionized by advances in high-throughput sequencing and multi-omics technologies, generating massive datasets that capture the taxonomic, functional, and metabolic diversity of microbial communities in agricultural soils; however, interpreting these complex datasets and translating them into practical agronomic insights remains challenging. Objectives To critically assess the role of artificial intelligence (AI) in soil microbiome-driven agriculture, focusing on methodological developments, prediction performance, existing limitations, and translational opportunities. Methods A narrative review was conducted to evaluate commonly used AI approaches, including random forest, gradient boosting, support vector machines, and deep learning architectures, alongside key microbiome data types such as amplicon sequencing, metagenomics, and functional gene profiling, with integration of environmental, agronomic, and meteorological datasets. Results The prediction of crop productivity, disease risk, nutrient cycling dynamics, and soil health indicators may be enhanced by AI-assisted integration of microbiome, soil physicochemical, and meteorological data, according to several studies. However, broad generalizations about predictive robustness and generalizability are limited by significant diversity in datasets, validation methods, and model architectures. Discussion To address these limitations, a five-phase implementation framework integrating centralized data systems, AI-driven analytics, multi-omics profiling, standardized soil sampling, and feedback-based model retraining within precision agriculture systems is proposed, providing a pathway for translating microbiome insights into field-scale decision support. Conclusion AI-enabled soil microbiome applications hold significant potential for sustainable agriculture, but future advancements will require large, multisite datasets, improved validation strategies, interpretable modeling approaches, and integration with digital agriculture technologies, highlighting both opportunities and practical constraints.