The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
Smart microalgae farming with internet-of-things for sustainable agriculture
Abstract
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.
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In the citing paper
“Wireless sensors integrated into IoT platforms enable automatic data transmission to cloud systems, where the data is processed to identify trends and detect potential problems in cultivation [246].”
From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [246]
In the citing paper
“In terms of environmental sustainability, ADT has the potential to transform how smart algal growth management is modeled from a physical perspective [12].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [12]
In the citing paper
“Correspondingly, ADT has also progressed and expanded in tandem with AI, IoT, and BD in the ACB sector to sustain the environment [1,11 -14].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [12]
In the citing paper
“According to [12], this is frequently the case with algal information modelling software that is incompatible with IoT integration.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [12]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [16]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [16]
In the citing paper
“An integrated, AI-driven cultivation framework offers the potential to improve biomass yield by 30–50%, reduce nutrient and energy consumption, and enhance process control accuracy for large-scale production [15,16,17].”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [16]
Citations
Cites
1 retracted
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Cited by
Sorted by citations
Khoo, K. S., Ahmad, I., Chew, K. W., Iwamoto, K., Bhatnagar, A., & Show, P. L. (2023) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101071 · Cited by 363
Self-citationOruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K. K., Gadhamshetty, V., & Bhattacharyya, D. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162797 · Cited by 211
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Self-citationRecent advances in CO2 fixation by microalgae and its potential contribution to carbon neutrality
Xu, P., Li, J., Qian, J., Wang, B., Liu, J., Xu, R., Chen, P. L., & Zhou, W. (2023) · Chemosphere · doi:10.1016/j.chemosphere.2023.137987 · Cited by 179
Pathmudi, V. R., Khatri, N., Kumar, S., Abdul-Qawy, A. S. H., & Vyas, A. K. (2023) · Scientific African · doi:10.1016/j.sciaf.2023.e01577 · Cited by 160
Metadata
- Authors
- Lim, H. R., Khoo, K. S., Chia, W. Y., Chew, K. W., Ho, S.-H., & Show, P. L.
- Affiliations
- University of Nottingham (GB)
- UCSI University (MY)
- University of Nottingham Malaysia Campus (MY)
- Xiamen University (CN)
- Harbin Institute of Technology (CN)
- Corresponding author
- Khoo, Kuan Shiong
- Ho, Shih-Hsin
- Show, Pau Loke
- Venue
- Biotechnology Advances
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 57
- Pages
- 107931
- ISSN
- 0734-9750 (ISSN-L)
- OpenAlex
- W4213272685
- Cited by
- 155
- References
- 90
- Citations by year
- 2026: 33 · 2025: 50 · 2024: 26 · 2023: 30 · 2022: 16
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biotechnology Advances (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Kementerian Pendidikan Malaysia
- Kementerian Pengajian Tinggi Malaysia
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Keywords
- Internet of Things, smart agriculture, microalgae cultivation, machine learning, process monitoring, sustainable agriculture
- MeSH terms
- Internet of Things, Agriculture, Artificial Intelligence, Humans, Internet, Microalgae
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