Model Embedded DRL for Intelligent Greenhouse Control
This addresses the problem of inefficient greenhouse control for crop producers, offering an incremental improvement over existing methods.
The paper tackles precise greenhouse environment control by proposing a model embedded deep reinforcement learning (MEDRL) framework that uses computer vision and crop growth models to optimize setpoints like temperature and humidity, resulting in greatly reduced costs compared to traditional methods.
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.