Multi-objective artificial pedosphere intelligence for microbial homeostasis-oriented governance of functional nanoparticle systems in complex ecosystems
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.
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BibTeX
@article{vidap2026multiobjective,
title = {{Multi-objective artificial pedosphere intelligence for microbial homeostasis-oriented governance of functional nanoparticle systems in complex ecosystems}},
author = {Raju Vidap and Ranjith Kumar Nadialli and Vinod Kumar Teriveedhi and K. Suresh Babu and Suresh K. Pulluru and Aele Manohar and Suresh Thondapu},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-75937-8},
url = {https://www.nature.com/articles/s41598-026-75937-8},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multi-objective artificial pedosphere intelligence for microbial homeostasis-oriented governance of functional nanoparticle systems in complex ecosystems
AU - Raju Vidap
AU - Ranjith Kumar Nadialli
AU - Vinod Kumar Teriveedhi
AU - K. Suresh Babu
AU - Suresh K. Pulluru
AU - Aele Manohar
AU - Suresh Thondapu
PY - 2026
DA - 2026/10/25
JO - Scientific Reports
DO - 10.1038/s41598-026-75937-8
UR - https://www.nature.com/articles/s41598-026-75937-8
AB - 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. .
SN - 2045-2322
ER -