Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production. (20th February 2021)

• Record Type: Journal Article
• Authors: Guo, Yu; Zhao, Huajian; Zhang, Shanhong; Wang, Yang; Chow, David
• Is Part Of: Journal of cleaner production. Volume 285(2021)
• Publication Date: 2021-02-20
• Subjects: Agricultural greenhouse -- Environment -- Modeling and optimization -- System identification -- Heuristic algorithm -- Multi-model integration; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.124843
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Resource-use efficiency and crop yield are significant factors in the management of agricultural greenhouse. Appropriate modeling methods effectively improve the control performance and efficiency of the greenhouse system and are conducive to the design of water and energy-saving strategies. Meanwhile, the extreme environment could be forecasted in advance, which reduces pests and diseases as well as provides high-quality food. Accordingly, the interest of the scientific community in greenhouse modeling and optimizing has grown considerably. The objective of this work is to provide guidance and insight into the topic by reviewing 73 representative articles and to further support cleaner and sustainable crop production. Compared to the existing literature review, this work details the approaches to improve the greenhouse model in the aspects of parameter identification, structure and process optimization, and multi-model integration to better model complex greenhouse system. Furthermore, a statistical study has been carried out to summarize popular technology and future trends. It was found that dynamic and neural network techniques are most commonly used to establish the greenhouse model and the heuristic algorithm is popular to improve the accuracy and generalization ability of the model. Notably, deep learning, the combination of "knowledge" and "data", and coupling between the greenhouse system elements have been considered as future valuable development. Highlights: A systematic literature review of modeling methods for agricultural greenhouse environment is provided. Improvement strategies of greenhouse environment and crop model have been summarized and classified. Applications of intelligent optimization algorithms for modeling greenhouse have been analyzed. Technical trends and valuable research direction to model greenhouse are discussed.