Analysis and prospect of the environmental control systems for greenhouse

Facility agriculture is one of the most important indicators to achieve efficient and high-quality crop production in modern agriculture. Optimal environmental parameters can also be adjusted to improve crop growth, yield, and quality in the greenhouse. Therefore, environmental control and regulation technologies have been widely used to achieve efficient vegetable production. This review aims to summarize the recent research status in the field of environmental regulation in greenhouses. New approaches were also proposed for future research priorities. Greenhouse environment system was gradually shifted from the manual and timed to the threshold, feedback, and intelligent control systems, with the rapid development of artificial intelligence (AI) technology. Firstly, the main properties of different control systems were evaluated from an application perspective. Among them, the threshold control was simple and widely used, but it failed to adjust the control strategy in time following the dynamic changes of the external environment, in order to meet the needs of crops for light, water, and nutrients. The feedback control shared the stable environment through feedback regulation but was unsuitable for the complex multivariable conditions. Intelligent control was widely used to balance the interaction between different environmental factors in modern greenhouses. Afterward, the intelligent control methods were investigated for greenhouse environments, including fuzzy, decoupling, neural network, and environmental control, according to the crop phenotype parameters. Specifically, the mathematical model was independent of the controlled object in the fuzzy control, and easy to handle with nonlinear issues. However, the outline fuzzy was difficult to handle the sudden disturbances in the regulation system. In decoupling control, the appropriate control strategies were designed to transform the multiple parameters with coupling effects into a single variable. The regulation model was also constructed to integrate the multiple environmental factors and crop physiological needs. The intelligent control of the environment was realized in the development of greenhouse agriculture. The Neural networks were used to extract valuable information from a large amount of greenhouse environment data, thus providing powerful tools for the regulation models. The intelligent models mainly included single-factor, multi-factor, and multi-objective environment regulation. The data-driven method was one of the research hotspots in the intelligent regulation of greenhouse environments. However, the universality and economic benefits were the key limiting factors of regulation models. Efficient and accurate acquisition of phenotypic parameters greatly contributed to the fine management of greenhouse environments, indicating the intuitive, real-time monitoring, and dynamic regulation. However, it was still lacking in the interaction between phenotype and multiple environmental factors, which failed to apply directly in greenhouse production. In addition, the existing environmental control systems were evaluated for the light, temperature, air, ventilation, and irrigation greenhouse. Research directions were proposed to urgently improve and optimize the control system. Finally, future research and development trends were also recommended to construct the greenhouse environmental regulation, considering disturbance factors. Environmental regulation models were developed using crop growth and phenotype evaluation. A "cloud-edge-end" system of greenhouse environmental regulation was established to integrate multiple models. This finding can provide new ideas and references for the subsequent development of environmental control systems in greenhouses.