{
  "abstract": "Soil microbial homeostasis is fundamental to sustainable agriculture and ecological resilience, yet existing machine learning approaches primarily emphasize prediction accuracy while overlooking microbial interactions, uncertainty quantification, ecological homeostasis, and decision-support for nanoparticle governance. This study proposes an Artificial Pedosphere Intelligence framework to predict the Microbial Homeostasis Deviation Score (MHDS) by integrating a Microbial Co-occurrence Graph Attention Network (MC-GAT), homeostasis-constrained learning, a Deep Kernel Gaussian Process (DKL), Bayesian Multi-Objective Optimization, and SHAP-based explainability within a unified ecological intelligence pipeline. The framework first constructs an ecological feature graph, learns biologically informed representations, estimates predictive uncertainty, and subsequently performs simulation-based optimization to identify environmentally balanced nanoparticle governance strategies. The model was implemented using Python 3.10 with PyTorch, PyTorch Geometric, GPyTorch, BoTorch, and SHAP, and evaluated on the Kaggle Soil Microbe Dataset containing 100,000 soil samples. The proposed framework achieved R² = 0.962, MSE = 0.007, and MAE = 0.016, representing an approximately 2.3% improvement in R² over the strongest baseline (0.94). These findings demonstrate that biologically informed graph intelligence combined with uncertainty-aware optimization provides accurate, interpretable, and scalable ecological decision support for sustainable soil management.\n\n.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Raju Vidap"
    },
    {
      "name": "Ranjith Kumar Nadialli"
    },
    {
      "name": "Vinod Kumar Teriveedhi"
    },
    {
      "name": "K. Suresh Babu"
    },
    {
      "name": "Suresh K. Pulluru"
    },
    {
      "name": "Aele Manohar"
    },
    {
      "name": "Suresh Thondapu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4s6k7a57vqnxk.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-75937-8",
  "fulltext_url": null,
  "id": "4s6k7a57vqnxk",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-75937-8",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7223568762",
  "pdf_url": "https://www.nature.com/articles/s41598-026-75937-8_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/4s6k7a57vqnxk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:34Z",
      "native_id": "10.1038/s41598-026-75937-8",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-75937-8"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "Multi-objective artificial pedosphere intelligence for microbial homeostasis-oriented governance of functional nanoparticle systems in complex ecosystems",
  "title_text": "Multi-objective artificial pedosphere intelligence for microbial homeostasis-oriented governance of functional nanoparticle systems in complex ecosystems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:34Z",
  "url": "https://research.draconitic.com/paper/4s6k7a57vqnxk",
  "venue": "Scientific Reports",
  "versions": []
}