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Related Experiment Video

Updated: Jan 7, 2026

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments
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Machine Learning Approaches to Assess Soil Microbiome Dynamics and Bio-Sustainability.

Roberta Pace1,2, Maurilia M Monti2, Salvatore Cuomo3

  • 1Department of Biology, University of the Study of Naples "Federico II", Naples, Italy.

Physiologia Plantarum
|January 5, 2026
PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) reveal soil microbial patterns for agricultural bio-sustainability. Unsupervised algorithms identified temporal and crop effects as key drivers, with fungi responding more to management than bacteria.

Keywords:
bio‐sustainbabilityhigh‐throughput sequencingmachine learning (ML)metagenomicssoil microbiota

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Area of Science:

  • Agricultural Science
  • Microbiology
  • Computational Biology

Background:

  • Soil microbial communities are crucial for agricultural bio-sustainability.
  • Their complexity makes predicting functional roles challenging.
  • High-throughput sequencing generates vast microbiome data requiring advanced analysis.

Purpose of the Study:

  • To apply unsupervised artificial intelligence (AI) and machine learning (ML) algorithms to analyze complex soil microbiome data.
  • To uncover hidden microbial patterns influencing soil biological status under different agricultural management strategies.
  • To differentiate temporal and treatment effects on bacterial and fungal communities.

Main Methods:

  • Soil samples were collected from four management strategies: conventional (C), conventional with organic matter (C+O), with microorganisms (M), and with both (M+O).
  • Metagenomic amplicon sequencing of 16S rRNA (bacteria) and ITS (fungi) genes was performed.
  • Unsupervised AI/ML algorithms including Principal Component Analysis (PCA), k-means clustering, and t-distributed stochastic neighbour embedding (t-SNE) were utilized.

Main Results:

  • Coherent temporal trajectories were observed in both bacterial and fungal communities.
  • Sampling time and crop presence were dominant drivers of community assembly.
  • Fungal communities showed higher plasticity and response to management compared to bacterial communities, which stabilized.
  • Management treatments had only subtle effects on overall community composition.

Conclusions:

  • Unsupervised ML workflows effectively disentangle temporal and treatment effects in soil microbiome data.
  • Fungal and bacterial guilds play complementary roles in soil ecosystems.
  • This study provides a foundation for developing predictive models to identify microbial indicators for bio-sustainable agriculture.