Harnessing recent progresses on wastewater treatment, food security, data analytics, and machine intelligence, we propose to study novel optimized technology-driven controlled-environment agriculture (CEA) systems that can achieve high areal vegetable productivity to increase the food and nutritional security in urban areas with low operating cost and reduced energy consumption.Our project focuses on two core CPS research areas, i.e., control and data analytics, inspired by the design and operations of a pilot testbed at Georgia Tech for coupling the water and nutrients in domestic wastewater (DWW) to high-productivity CEAs. Food production in the CEAs must warrant the high cost of land in urban areas, which makes it necessary to reduce the total DWW-CEA operating cost and increase productivity. However, it is highly challenging to control and optimize this complex system of subsystems. In our case, we need to coordinate the Pilot-Plant and Pilot-Farm, examine their inter-correlation, and support dynamic and robust optimal decisions to achieve the highest production yield, while simultaneously satisfying various performance specifications on nutrient compositions, operating cost and energy consumption, and meeting other safety requirements of DWW-CEAs. Moreover, the profound impact of numerous operating conditions and parameters on the vegetable phenotype, yield and nutrient compositions during different growth periods need to be thoroughly understood. We have to move from model-driven CPS fundamentals to an integrated data-driven model-based approach. The objective of this 3-year interdisciplinary project is to maximize food productivity and nutrition while minimizing cost, energy and waste. The research is organized as four thrusts, each objective-driven and delineated as follows.InThrust 1, first-principles water models are adapted to the physical system and calibrated before on-site experimental validation. As first-principles models are not available for hydroponic agriculture, inThrust 2we adopt a hybrid approach where parameter-dependent biochemical reactions are supported by machine-learning algorithms with the purpose of parameter estimation and capture of possible un-modeled dynamics. A distinguishing feature ofThrust 2is the implementation of non-invasive spectroscopy and imaging to gain additional plant morphology and nutrition information. The subsystems (e.g., water and hydroponics) are then integrated as a system-wide model and validated experimentally. InThrust 3, we devise control algorithms with the purpose of achieving desired closed-loop performance despite the presence of disturbances and/or uncertain dynamics. As a part ofThrust 4, the resulting CPS is implemented in simulation software and experimentally validated to determine a feasible deployment scale. We expect that our novel, foundational research contributions will be immediately transferable to the water, waste, agriculture and remote sensing industries, but benefit other complex system control applications entirely.