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AI-Driven Controlled Environment Agriculture as Resilient Infrastructure for U.S. Fresh-Produce Supply Chains

Climate volatility, regional production concentration, labor constraints, cyber risk, and dependence on long-distance fresh-produce supply chains expose vulnerabilities in U.S. fresh-produce and specialty-crop systems. Controlled environment agriculture (CEA) can reduce some exposure by moving selected production into protected, sensor-rich environments, but recent failures in venture-backed vertical farming show that CEA cannot be treated as a universal food-security solution. This paper proposes the Controlled Environment Agriculture Resilience Infrastructure Framework, Version 2.0 (CEA-RIF 2.0), for evaluating AI-driven CEA as targeted regional fresh-produce continuity infrastructure. The framework assesses seven dimensions: supply continuity, climate isolation, energy and grid integration, water and nutrient circularity, cyber-physical reliability, economic viability, and governance and deployment. Drawing on U.S. government reports, peer-reviewed CEA and energy literature, demand-response research, cybersecurity standards, international smart-agriculture programs, 2025-2026 financing and policy signals, and public autonomous-greenhouse datasets, the paper argues that AI creates resilience value only when it improves measured operational outcomes such as climate stability, energy flexibility, yield consistency, anomaly detection, labor productivity, and safe recovery from faults. The analysis reframes AI-driven CEA as a cyber-physical infrastructure problem: energy-aware, grid-interactive, secure, interoperable, regionally distributed, financially disciplined, and connected to public resilience goals. The paper concludes with a research agenda for interagency testbeds, open datasets, standardized metrics, demand-response pilots, and cyber-physical reference architectures.

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