Optimizing urban agriculture with digital twins: a pathway to enhanced food security and sustainability
This conceptual paper proposes the Urban Agriculture Digital Twin (UA-DT), a four-layer cyber-physical framework for real-time monitoring, predictive analytics, and city-scale food system simulation across diverse urban agriculture (UA) systems. Rapid urbanization intensifies pressure on urban food supply chains, yet the representative Digital Twin (DT) applications in agriculture reviewed here focus on large-scale, single-site controlled-environment systems and do not integrate supply-chain dynamics, policy simulation, or equity-conscious design for diverse UA typologies. Developed through a conceptual framework methodology integrating DT architecture, precision agriculture modeling, and urban systems theory, the UA-DT combines IoT sensing, cloud data infrastructure, and hybrid mechanistic/AI-driven models; its logical feasibility is demonstrated via an illustrative software simulation (Eclipse Ditto, Azure IoT, Python) for a community rooftop garden. The UA-DT offers a testable architecture to enhance resource efficiency, supply-chain resilience, and decision-making, supporting municipal policy simulation and democratizing agronomic intelligence for community and smallholder farmers. To contextualize the framework's potential, a structured narrative synthesis of analogous controlled-environment agriculture literature identifies literature-derived benchmark ranges—20–40% water savings, 15–25% fertilizer reduction, up to 30% post-harvest loss reduction, and 3–7 day early-warning lead time—as targets that a fully validated UA-DT deployment would be expected to approach; these are informed projections from prior literature, not empirical findings of this study.