Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production

Guo, Yu, Zhao, Huajian, Zhang, Shanhong, Wang, Yang and Chow, David ORCID: 0000-0002-5963-6228 (2021) Modeling and optimization of environment in agricultural greenhouses for improving cleaner and sustainable crop production. Journal of Cleaner Production, 285. p. 124843.

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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.

Item Type:	Article
Uncontrolled Keywords:	Agricultural greenhouse, Environment, Modeling and optimization, System identification, Heuristic algorithm, Multi-model integration
Divisions:	Faculty of Humanities and Social Sciences > School of the Arts
Depositing User:	Symplectic Admin
Date Deposited:	16 Jun 2021 09:28
Last Modified:	18 Jan 2023 22:35
DOI:	10.1016/j.jclepro.2020.124843
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3125715