Smart microalgae farming with internet-of-things for sustainable agriculture.

Autor: Lim HR; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia., Khoo KS; Faculty of Applied Sciences, UCSI University, UCSI Heights, 56000 Cheras, Kuala Lumpur, Malaysia. Electronic address: kuanshiong.khoo@ucsiuniversity.edu.my., Chia WY; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia., Chew KW; School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China. Electronic address: kitwayne.chew@xmu.edu.my., Ho SH; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China. Electronic address: stephen6949@hit.edu.cn., Show PL; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia. Electronic address: PauLoke.Show@nottingham.edu.my.
Jazyk: angličtina
Zdroj: Biotechnology advances [Biotechnol Adv] 2022 Jul-Aug; Vol. 57, pp. 107931. Date of Electronic Publication: 2022 Feb 22.
DOI: 10.1016/j.biotechadv.2022.107931
Abstrakt: Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era.
(Copyright © 2022 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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