Evaluation of Real-Time Monitoring on the Growth of Spirulina Microalgae: Internet of Things and Microalgae Technologies
Abstract
Microalgae farming is still in its infancy phase to implement Internet of Things (IoT) technologies. Spirulina microalgae was selected to be investigated in this study because it is the most commercially viable microalgae for large-scale production. There are various methods to measure the biomass concentration of microalgae. However, the conventional methods are time consuming, laborious, or expensive. Therefore, this research aims to integrate IoT with a biomass concentration sensor (TCS3200), to monitor the biomass concentration of Spirulina in real time and remotely through the cloud-based system (ThingSpeak). The TCS3200 sensor was programmed and set up to measure the absorbance of the Spirulina culture in a 10-L cultivation tank. The data was collected and stored in the cloud Thingspeak platform. Besides, additional parameters, such as temperature, light intensity, and water level, were monitored to gain a comprehensive understanding of the entire cultivation process. These sensors were all controlled by a Lolin NodeMCU ESP8266 microcontroller. The TCS3200 sensor was found to be better suited for measuring low-biomass concentrations, while a UV-Vis spectrophotometer was able to measure higher biomass concentrations after the sample was diluted. Real-time monitoring of these parameters allows for timely adjustments to be made to the cultivation conditions to optimize the growth and productivity of the Spirulina culture and detect any problems or abnormalities in the process. The use of IoT technologies in the cultivation of microalgae can improve the efficiency and sustainability of the process and provide valuable data for further research and development.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The hurdles associated with actuation were not widely discussed in the literature because the required algal digital twin technology has not yet been completely established in reality.
“The hurdles associated with this issue were not widely discussed in the literature because the required ADT technology has not yet been completely established in reality [45,65].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [45]
Synergistic combination of IoT architecture and powerful AI algorithms holds great potential for substantial improvements in analysis of complex real-time process systems such as ACB.
“The synergistic combination of IoT architecture and powerful AI algorithms holds great potential for substantial improvements in the analysis of complex realtime process systems such as ACB [45].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [45]
Cites
Precision agriculture using IoT data analytics and machine learning
Akhter, R., & Sofi, S. A. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.05.013 · Cited by 560 · see citing works
Intelligent Agriculture Greenhouse Environment Monitoring System Based on IOT Technology
Liu, D., Xin, C., Chongwei, H., et al. (2015) · International Conference on Intelligent Transportation, Big Data and Smart City · doi:10.1109/icitbs.2015.126 · Cited by 180 · see citing works
Nayyar, A., & Puri, V. (2016) · doi:10.1201/9781315364094-121 · Cited by 173 · see citing works
IoT-Based Strawberry Disease Prediction System for Smart Farming
Kim, S.-H., Lee, M., & Shin, C. (2018) · Sensors · doi:10.3390/s18114051 · Cited by 171 · see citing works
Smart microalgae farming with internet-of-things for sustainable agriculture
Lim, H. R., Khoo, K. S., Chia, W. Y., Chew, K. W., Ho, S.-H., & Show, P. L. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.107931 · Cited by 155 · see citing works
Self-citationInsect pollination as a key factor for strawberry physiology and marketable fruit quality
Wietzke, A., Westphal, C., Gras, P., et al. (2018) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2018.01.036 · Cited by 141 · see citing works
Kumar, M., Kulshreshtha, J., & Singh, G. P. (2011) · Brazilian Journal of Microbiology · doi:10.1590/s1517-83822011000300034 · Cited by 129 · see citing works
Sandnes, J. M., Ringstad, T., Wenner, D., Heyerdahl, P. H., Källqvist, T., & Gislerød, H. R. (2006) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2005.08.034 · Cited by 99 · see citing works
Liquid biphasic flotation for the purification of C-phycocyanin from Spirulina platensis microalga
Chew, K. W., Chia, S. R., Rambabu, K., Tao, Y., Chu, D., & Show, P. L. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.121519 · Cited by 94 · see citing works
Self-citationMethods to quantify biological contaminants in microalgae cultures
Di Caprio, F. (2020) · Algal Research · doi:10.1016/j.algal.2020.101943 · Cited by 87 · see citing works
Danesi, E. D. G., Rangel-Yagui, C. O., Sato, S., & Carvalho, J. C. M. D. (2011) · Brazilian Journal of Microbiology · doi:10.1590/s1517-83822011000100046 · Cited by 67 · see citing works
Chen, C., Kao, P.-C., Tan, C. H., Show, P. L., Cheah, W. Y., Lee, W.-L., Ling, T. C., & Chang, J. (2016) · Biochemical Engineering Journal · doi:10.1016/j.bej.2016.04.009 · Cited by 65 · see citing works
Self-citationInternet of Things (IoT) Based Water Level Monitoring System for Smart Village
Malche, T., & Maheshwary, P. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-2750-5_32 · Cited by 59 · see citing works
Nguyen, B. T., & Rittmann, B. E. (2018) · Algal Research · doi:10.1016/j.algal.2018.03.013 · Cited by 56 · see citing works
Benavides, M., Mailier, J., Hantson, A.-L., Muñoz, G., Vargas, A., Van Impe, J., & Vande Wouwer, A. (2015) · Sensors · doi:10.3390/s150304766 · Cited by 54 · see citing works
Effects of heat waves on urban warming across different urban morphologies and climate zones
Harmay, N. S. M., & Choi, M. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108677 · Cited by 50 · see citing works
Effects of shear flow on photosynthesis in a dilute suspension of microalgae
Mitsuhashi, S., Hosaka, K., Tomonaga, E., Muramatsu, H., & Tanishita, K. (1995) · Applied Microbiology and Biotechnology · doi:10.1007/bf00171956 · Cited by 47 · see citing works
Cultivation of Spirulina platensis in different selective media
Dineshkumar, R., Narendran, R., & Sampathkumar, P. (2016) · Cited by 39 · see citing works
A comparative study on three quantitating methods of microalgal biomass
Lu, L., Yang, G., Zhu, B., & Pan, K. (2017) · Indian Journal of Marine Sciences · Cited by 39 · see citing works
Zhu, J., & Wakisaka, M. (2019) · Journal of Nutritional Science and Vitaminology · doi:10.3177/jnsv.65.s212 · Cited by 27 · see citing works
Design of greenhouse environment monitoring system based on Wireless Sensor Network
Liu, L., & Zhang, Y. (2017) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar.2017.7942739 · Cited by 24 · see citing works
Automation in Agriculture - Securing Food Supplies for Future Generations
Hussmann, S. H. (2018) · InTech eBooks · doi:10.5772/intechopen.69016 · Cited by 23 · see citing works
Beyaz, A., & Gül, V. (2022) · Brazilian Archives of Biology and Technology · doi:10.1590/1678-4324-2022220172 · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wu, Y., Shan, L., Zhao, W., & Lu, X. (2025) · Marine Drugs · doi:10.3390/md23050184 · Cited by 59 · see citing works
Sheik, A. G., Kumar, A., Ansari, F. A., Raj, V., Peleato, N. M., Patan, A. K., Kumari, S., & Bux, F. (2024) · Algal Research · doi:10.1016/j.algal.2024.103779 · Cited by 37 · see citing works
Internet of things (IoT) use for remote monitoring of algal bioreactors
Gotovtsev, P., Sergeeva, Y., Sukhinov, D., Petrova, M., Vishnevskaya, M., Shestakova, A., Rogov, A., Romanov, A., Gorin, K., & Vasilov, R. (2025) · Algal Bioreactors · doi:10.1016/b978-0-443-14058-7.00044-0 · Cited by 4 · see citing works
Zhao, N., Chu, Y., Huang, Q., Qin, S., Cui, H., & Zhang, W. (2026) · Algal Research · doi:10.1016/j.algal.2026.104629 · Cited by 2 · see citing works
- Authors
- Lim, H. R., Khoo, K. S., Chew, K. W., Teo, M. Y. M., Ling, T. C., Alharthi, S., Alsanie, W. F., & Show, P. L.
- Affiliations
- University of Nottingham Malaysia Campus (MY)
- Chettinad Academy of Research and Education (IN)
- Nanyang Technological University (SG)
- UCSI University (MY)
- University of Malaya (MY)
- Taif University (SA)
- Wenzhou University (CN)
- ORCID
- Venue
- IEEE Internet of Things Journal
- Published
- 2024-01-15
- Volume
- 11
- Issue
- 2
- Pages
- 3274-3281
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4384787475
- Cited by
- 22
- References
- 23
- Citations by year
- 2026: 2 · 2025: 12 · 2024: 7 · 2023: 1
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- Biolina Corporation Sdn Bhd., Malaysia (NVEC0018)
- UCSI University for the Research Excellence and Innovation Grant (REIG-FAS-2022/003)
- Taif University Researchers Supporting Project, Taif University, Saudi Arabia (TURSP-HC2023/3)
- Khalifa University (8474000580)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Life Sciences › Agricultural and Biological Sciences › Aquatic Science › Innovations in Aquaponics and Hydroponics Systems
- Keywords
- Spirulina, microalgae cultivation, Internet of Things, real-time monitoring, ThingSpeak, cloud storage, NodeMCU ESP8266, UV-Vis spectroscopy, remote monitoring, water level monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
