2019/12/01 by Tinghao Zhang, Zhang, Tinghao, LI Jing-xu +9
Agricultural and Biological Sciences · Environmental Science · #FOS: Computer and information sciences #FOS: Electrical engineering #Greenhouse Technology and Climate Control #Horticultural and Viticultural Research #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Plant Water Relations and Carbon Dynamics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1912.00020
openalex publication_date 2019/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.