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Prediction and control of greenhouse temperature: Methods, applications, and future directions

文献类型： 外文期刊

作者： Yu, Jingxin 1 ; Sun, Congcong 2 ; Zhao, Jinpeng 1 ; Ma, Lushun 5 ; Zheng, Wengang 1 ; Xie, Qiuju 6 ; Wei, Xiaoming 1 ;

作者机构： 1.Natl Engn Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Wageningen Univ, Agr Biosyst Engn Grp, NL-6700 AA Wageningen, Netherlands

3.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment Technol, Beijing 100097, Peoples R China

4.Xian Technol Univ, Coll Mech & Elect Engn, Xian 710021, Peoples R China

5.Shandong Agr Univ, Sch Informat Sci & Engn, Tai An 271018, Peoples R China

6.Northeast Agr Univ, Coll Elect & Informat, Harbin 150030, Peoples R China

关键词： Greenhouse; Prediction model; Temperature control; Artificial intelligence; Hybrid models

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 237 卷

页码：

收录情况： SCI

摘要： Greenhouse cultivation is crucial for global food security and sustainable agricultural development. To maintain efficient greenhouse crop production, temperature management is the core. For a better understanding of the current status, methods, applications, and future directions for greenhouse prediction and control, this paper provides a comprehensive review of different technologies for greenhouse temperature management. The contributions cover three key aspects: (1) The role of different sensing techniques, the Internet of Things, wireless sensor networks, and multimodal data fusion technologies in supporting greenhouse temperature management; (2) Status and advantages of traditional models, artificial intelligent (AI)-based models, and hybrid models for greenhouse temperature prediction; (3) Applicable scenarios and limitations of different control methods, such as fuzzy logic, model predictive control (MPC), reinforcement learning (RL) and intelligent systems for greenhouse temperature control. Comparative analysis demonstrates that deep learning models excel in greenhouse temperature prediction, while fuzzy logic, MPC, and RL exhibit unique strengths in greenhouse temperature control. Despite the potential demonstrated by advanced AI technology in greenhouse temperature management, practical applications continue to encounter challenges such as model robustness, interpretability, and computational efficiency. To fully exploit the potential of AI, future research should focus on developing plant-centric AI models, exploring the application of AI in sustainable energy management of greenhouses, and developing digital twin models to promote the development of AI and greenhouse technology, creating a new paradigm for sustainable, resilient, and intelligent agriculture. This review offers a comprehensive perspective on optimizing greenhouse environment management, which is crucial to address challenges in food security and climate change.

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