This paper presents a novel workflow for the design of an adaptive model-based controller to optimize the time and energy consumption for plant cultivation combined with on- line analysis and estimation of model parameters based on scarce data. A non-linear model of lettuce growth is subject to sensitivity analysis of selected parameters to determine the effective sequence and time horizon of infrequent data sampling of plant physiological properties. In the designed measurement campaign, the parameter estimation is performed to update the model parameter space, improving the accuracy of plant growth predictions and control efficiency. The implementation of run-time-updated model in a predictive control framework leads to minimization of the energy-related cost and the full-growth time of the plant. Simulations show promising results in minimizing the time required to the desired plant yield.