Abstract: This study examines the integration of artificial intelligence (AI) into regenerative agriculture (RA) to improve both environmental and economic outcomes. The primary objective was to evaluate how AI tools—such as neural networks, remote sensing, and decision support systems—can improve soil health, biodiversity, and farm profitability. A comparative case design was modelled to examine three systems: conventional, regenerative, and AI-enhanced regenerative farms. Using Python-based machine learning algorithms (Random Forest, XGBoost), Sentinel-2 imagery, and sensor data, key metrics, including soil organic carbon (SOC), microbial biomass, biodiversity index, and.......
Keywords: Artificial Intelligence, Regenerative Agriculture, Soil Organic Carbon, Biodiversity Index, Decision Support Systems, Sustainable Farming
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