A Review of Environmental Control Strategies and Models for Modern Agricultural Greenhouses

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 5; p. 1388
• Main Authors: Chen, Shuailiang, Liu, Aolong, Tang, Fei, Hou, Pei, Lu, Yanli, Yuan, Pei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.02.2025; MDPI
• Subjects: Agricultural production; Carbon dioxide; Control algorithms; control strategies; Controllers; Cooling; Energy consumption; Energy efficiency; Food; greenhouse models; Greenhouses; Growth models; Heat; Heating; Humidity; Lighting; Mathematical models; Microclimate; modern agricultural greenhouse; Neural networks; Plant growth; Review; Simulation; Temperature; Ventilation; modern agricultural greenhouse; greenhouse models; control strategies
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25051388

As crucial sites for optimizing crop growth conditions, greenhouses have gained increasing favor among scholars due to their potential to significantly enhance food production. Greenhouse control involves regulating environmental parameters such as temperature, humidity, light, and CO2 concentration to ensure an optimal growth environment for crops while conserving energy. This paper provides an overview of various strategies for controlling greenhouse environments, encompassing structural control, environmental parameter management, and control algorithms, and points out that the integration of artificial neural networks with various optimization algorithms is a future trend. Additionally, it delves into the exploration of greenhouse microclimate models and crop growth models, noting that current models focus on some of the internal environmental parameters and that the models rely on empirical parameters. Therefore, multi-scale coupling of greenhouse models is the way forward. Furthermore, it provides insights into how to achieve sustainable energy use in greenhouses, and the application of digital twin technology in greenhouses is promising.