PREDICTIVE MODELING OF SOIL MICROBIOME HEALTH USING AGENTIC AI FRAMEWORKS

This study presented a comprehensive framework for predictive modelling of soil microbiome health using agentic AI architectures. The findings demonstrate that autonomous multi-agent systems outperform conventional machine learning models in predictive accuracy, adaptability, and temporal forecasting. By integrating reinforcement learning, deep neural networks, and explainable AI methods, the proposed framework effectively captured complex interactions between microbial communities and environmental conditions. The ability of intelligent agents to autonomously monitor, predict, and optimize soil health represents a significant advancement in precision agriculture and environmental sustainability. At the same time, challenges related to computational efficiency, data standardization, and large-scale deployment must be addressed before widespread adoption becomes practical.

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