Integrating Artificial Intelligence and Soil Ecology to Enhance Plant Resilience under Environmental Stress

Abstract

Environmental stresses such as drought, salinity, nutrient deficiency, and temperature extremes significantly reduce agricultural productivity and threaten global food security. Soil ecology plays a critical role in plant resilience by influencing nutrient availability, microbial activity, and root health. Recent advances in Artificial Intelligence (AI) offer powerful tools for analyzing complex soil–plant–environment interactions. This study proposes an integrated framework combining AI-driven analytics with soil ecological parameters to enhance plant resilience under environmental stress conditions. Machine learning models are employed to predict plant stress responses using soil physicochemical properties, microbial diversity indices, and climatic data. Experimental results demonstrate that AI-assisted soil ecology modeling improves stress prediction accuracy and supports informed decision-making for sustainable agriculture. The findings highlight the potential of AI–soil ecology integration as a transformative approach for climate-resilient crop management.

Keywords: Artificial Intelligence, Soil Ecology, Plant Resilience, Environmental Stress, Machine Learning, Sustainable Agriculture