Model Embedded DRL for Intelligent Greenhouse Control

Autor:	Zhang, Tinghao, Li, Jingxu, Li, Jingfeng, Wang, Ling, Li, Feng, Liu, Jie
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted to AAAI-20 Workshop on Artificial Intelligence of Things
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.00020 Zobrazit plný text záznamu View this record from Arxiv