Over nine billion people will have to be fed fresh and healthy food in 2050 according to United Nations. This puts pressure on the horticulture sector, responsible for a large portion of the world’s food production. The literature has shown that automatic optimal control algorithms are able to make better use of resources and can even outperform growers in terms of resource efficiency.

While applications of automatic optimal control methods show promising results, few are made adaptive to the greenhouse-crop system, assuming the system to be time-invariant. That is, the prediction models or techniques used in the controllers are not updated to adapt better to the evolving greenhouse-crop system. This thesis sets a first step towards this on-line system adaptation: it investigates the possibility to use a linear state-space model to estimate the non-linear greenhouse-crop systems dynamics around an operating point, for the purpose of automatic optimal climate control. In this work, a linear state-space model is identified and is implemented in an automatic optimal controller based on Model Predictive Control (MPC). Moreover, a greenhouse-crop simulation is built from state-of-the-art models based on first-principles. This ground-truth simulation is used to generate data and test the proposed control method.