  Automatic control of greenhouse crop production is of great interest owing to
the increasing energy and labor costs. In this work, we use two-level control,
where the upper level generates suitable reference trajectories for states and
control inputs based on day-ahead predictions. These references are tracked in
the lower level using Nonlinear Model Predictive Control (NMPC). In order to
apply NMPC, a model of the greenhouse dynamics is essential. However, the
complex nature of the underlying model including discontinuities and
nonlinearities results in intractable computational complexity and long
sampling times. As a remedy, we employ NMPC as a data generator to learn the
tracking control policy using deep neural networks. Then, the references are
tracked using the trained Deep Neural Network (DNN) to reduce the computational
burden. The efficiency of our approach under real-time disturbances is
demonstrated by means of a simulation study.
