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 unmodeled 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.