Machine Learning Approaches to Assess Soil Microbiome Dynamics and Bio-Sustainability.

ABSTRACT: Understanding soil microbiota dynamics is essential for enhancing bio-sustainability in agriculture, yet the complexity of microbial communities hampers the prediction of their functional roles. Artificial intelligence (AI) and machine learning (ML) offer powerful tools to analyse high-dimensional microbiome data generated by high-throughput sequencing. Here, we apply unsupervised AI-based algorithms to uncover microbial patterns that are not immediately recognisable but are crucial for characterising the biological status of agricultural soils. Soil samples were collected from a site in Northern Italy managed under four strategies: conventional farming without organic matter (C), with organic matter (C + O), with beneficial microorganisms but without organic matter (M), and with both bene

SUBMITTER: Pace R 

PROVIDER: S-EPMC12766673 | biostudies-literature | 2026 Jan-Feb

REPOSITORIES: biostudies-literature