农机化研究2025，Vol.47Issue(5)：22-27,6.DOI:10.13427/j.issn.1003-188X.2025.05.004
基于深度强化学习的温室环境协调控制系统设计
Design of Greenhouse Environment Coordinated Control System Based on Deep Reinforcement Learning
摘要
Abstract
In response to the issues of high energy consumption and low nutrient utilization caused by the lack of coordi-nation in greenhouse temperature,light,and water-fertilizer control,proposed a deep reinforcement learning-based method for coordinated control of the greenhouse environment.With energy consumption and photosynthetic rate as the optimization objectives,a deep reinforcement learning algorithm was used to train the model to optimize the target values of temperature and light regulation.The impact of different nutrient irrigation levels on crop growth was analyzed to deter-mine a dynamic adjustment method for irrigation volume.The software and hardware of the coordinated control system of greenhouse environment based on deep reinforcement learning were developed.The experimental results showed that the method can coordinate the control of greenhouse temperature,light and water and fertilizer environmental factors,and compared with the traditional control method,the energy consumption of environmental regulation was reduced by 8.1％,the amount of nutrient solution irrigation was reduced by 7.9％,and the photosynthetic rate was increased by 2.7％,which can provide decision support for the efficient control of greenhouse environment.关键词
温室/深度强化学习/协调控制/光合速率/能耗Key words
greenhouse/deep reinforcement learning/coordinated control/photosynthetic rate/energy consumption分类
农业科技引用本文复制引用
左志宇,牟晋东,毛罕平,韩绿化,胡建平,张晓东,金文帅..基于深度强化学习的温室环境协调控制系统设计[J].农机化研究,2025,47(5):22-27,6.基金项目
国家大宗蔬菜产业技术体系岗位专家任务项目(CARS-23-D-03) （CARS-23-D-03）
江苏省农业科技自主创新项目(CX(20)1005) （CX(20）
