  Greenhouse environment is the key to influence crops production. However, it
is difficult for classical control methods to give precise environment
setpoints, such as temperature, humidity, light intensity and carbon dioxide
concentration for greenhouse because it is uncertain nonlinear system.
Therefore, an intelligent close loop control framework based on model embedded
deep reinforcement learning (MEDRL) is designed for greenhouse environment
control. Specifically, computer vision algorithms are used to recognize growing
periods and sex of crops, followed by the crop growth models, which can be
trained with different growing periods and sex. These model outputs combined
with the cost factor provide the setpoints for greenhouse and feedback to the
control system in real-time. The whole MEDRL system has capability to conduct
optimization control precisely and conveniently, and costs will be greatly
reduced compared with traditional greenhouse control approaches.
