Agentic artificial intelligence-driven digital twin for real-time irrigation control with fuzzy sustainability objectives

Irrigation management in modern agriculture faces simultaneous challenges, including water scarcity, climate uncertainty, and the need for long-term sustainability. In this study, an integrated framework for real-time irrigation control is presented, in which a digital twin is not merely used as a monitoring or simulation tool but is directly embedded in the decision-making and control loop through an agent-based fuzzy multi-objective optimization mechanism. Unlike conventional smart irrigation approaches that rely on static thresholds or offline optimization, the proposed framework enables adaptive, context-based decision updates by continuously integrating physical system feedback into a dynamic optimization engine. The decision-making agent, by simultaneously assessing soil, climate, and plant growth conditions, generates irrigation policies that balance water consumption, crop growth, and environmental sustainability requirements under fuzzy uncertainty. Experimental results show that using dynamic feedback in the digital twin framework improves the multi-objective performance index by more than 12% compared to the static state and significantly reduces control fluctuations. Convergence, stability under uncertainty, and parameter sensitivity analyses also indicate that the proposed framework can establish a sustainable balance across water resource utilization, crop yield, and environmental considerations. The findings indicate that this approach can provide a practical and reliable platform for transitioning to smart, adaptive, and sustainable irrigation systems.