Deep learning approaches have attracted a lot of interest and competition in a variety of fields. The major goal is to design an effective deep learning process in automatic modeling and control field. In this context, our aim is to ameliorate the modeling and control tasks of the greenhouse using deep neural network techniques. In order to emulate the direct dynamics of the system an Elman neural network has been trained and a deep multi-layer perceptron (MLP) neural network has been formed in order to reproduce its inverse dynamics and then used as a neural controller. This later was been associated in cascade with the deep Elman neural model to control the greenhouse internal climate. After performing experiments, simulation results show that the best performances were obtained when we have used a neural controller having two hidden layers and an Elman neural model with two hidden and context layers.
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