Smart sensing and anomaly detection for microalgae culture based on LoRaWAN sensors and LSTM autoencoder
Open accessAbstract
The increasing demand for sustainable aquaculture solutions has accelerated interest in microalgae as an alternative fish feed source. This study presents a novel integration of Long-Range Wide Area Network (LoRaWAN) sensors and a Long Short-Term Memory (LSTM) autoencoder model to enable real-time monitoring and anomaly detection in 300L outdoor microalgae cultivation. A solar-powered Internet of Things (IoT) sensor and LoRaWAN system were developed to monitor key water quality parameters such as pH, water temperature, electrical conductivity (EC), and oxidation–reduction potential (ORP). ORP was identified as a sensitive alternative for monitoring microalgae growth dynamics and contamination. The LSTM autoencoder, trained on normal ORP data, effectively distinguished abnormal culture patterns based on reconstruction errors, achieving high precision (1.000), recall (0.944), F1-score (0.971), and AUC-ROC (1.000). The addition of a moving average filter improved data stability for model training of filtered sensor signals. A bespoke visual reconstruction error heatmap further simplified anomaly interpretation for end-users. This framework advances smart aquaculture by enabling timely intervention, supporting sustainable microalgae production, and aligning with global food security and environmental sustainability goals.
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Even with the support of digital tools, environmental and nutritional factors such as temperature, pH, nitrogen and phosphorus balance, and availability of inorganic and organic carbon continue to be decisive for productivity, requiring continuous monitoring and increasingly sophisticated control strategies.
“Moreover, even with the support of digital tools, environmental and nutritional factors—such as temperature, pH, nitrogen and phosphorus balance, as well as the availability of inorganic and organic carbon—continue to be decisive for productivity, requiring continuous monitoring and increasingly sophisticated control strategies (Gil et al., 2025; Soon et al., 2025).”
From Microalgae biotechnology and its role in sustainable and healthy food design (2025) · cites this paper as Soon et al., 2025
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687 · see citing works
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · cited 3× · see citing works
“A further drawback of classical ML algorithms is their dependence on human feature extraction from time series data (Yamashita et al. 2018).”
A comparative analysis of gradient boosting algorithms
Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09896-5 · Cited by 2,998 · see citing works
Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning
Heaton, J. (2017) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-017-9314-z · Cited by 1,062 · see citing works
Unsupervised real-time anomaly detection for streaming data
Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.04.070 · Cited by 1,004 · see citing works
Deep Learning for Time Series Anomaly Detection: A Survey
Darban, Z. Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2024) · ACM Computing Surveys · doi:10.1145/3691338 · Cited by 743 · see citing works
Shehadeh, A., Alshboul, O., Mamlook, R. E. A., & Hamedat, O. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103827 · Cited by 421 · see citing works
Microalgae cultivation in photobioreactors: sustainable solutions for a greener future
Abdur Razzak, S., Bahar, K., Islam, K. M. O., Haniffa, A. K., Faruque, M. O., Hossain, S. M. Z., & Hossain, M. M. (2024) · Green Chemical Engineering · doi:10.1016/j.gce.2023.10.004 · Cited by 366 · see citing works
Advances in machine learning and IoT for water quality monitoring: A comprehensive review
Essamlali, I., Nhaila, H., & Khaïli, M. E. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e27920 · Cited by 273 · see citing works
Ahmad, A., Hassan, S. W., & BANAT, F. A. (2022) · Bioengineered · doi:10.1080/21655979.2022.2061148 · Cited by 257 · see citing works
Employing deep learning and transfer learning for accurate brain tumor detection
Mathivanan, S. K., Sonaimuthu, S., Murugesan, S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-57970-7 · Cited by 243 · see citing works
Microalgae for biotechnological applications: Cultivation, harvesting and biomass processing
Morais, W. G., Gorgich, M., Corrêa, P. S., Martins, A. A., Mata, T. M., & Caetano, N. S. (2020) · Aquaculture · doi:10.1016/j.aquaculture.2020.735562 · Cited by 220 · see citing works
Biological indicators for pollution detection in terrestrial and aquatic ecosystems
Zaghloul, A., Saber, M., Gadow, S. I., & Awad, F. (2020) · Bulletin of the National Research Centre/Bulletin of the National Research Center · doi:10.1186/s42269-020-00385-x · Cited by 211 · see citing works
Khademi, Z., Ebrahimi, F., & Kordy, H. M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105288 · Cited by 185 · see citing works
Hasan, M. K., Alam, M. A., Roy, S., Dutta, A., Jawad, M. T., & Das, S. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100799 · Cited by 159 · see citing works
Wang, F., Jiang, Y., Zhang, R., Wei, A., Xie, J., & Pang, X. (2025) · Sensors · doi:10.3390/s25010190 · Cited by 119 · see citing works
Yoshimura, T., Okada, S., & Honda, M. (2013) · Bioresource Technology · doi:10.1016/j.biortech.2013.01.095 · Cited by 111 · see citing works
A Survey on Machine Learning Approaches and Its Techniques:
Thomas Rincy, N., & Gupta, R. (2020) · IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) · doi:10.1109/sceecs48394.2020.190 · Cited by 110 · see citing works
The application of microalgae biomass and bio-products as aquafeed for aquaculture
Chen, F., Leng, Y., Lu, Q., & Zhou, W. (2021) · Algal Research · doi:10.1016/j.algal.2021.102541 · Cited by 106 · see citing works
A review on machine learning approaches for microalgae cultivation systems
Syed, T., Krujatz, F., Ihadjadene, Y., Mühlstädt, G., Hamedi, H., Mädler, J., & Urbas, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108248 · Cited by 92 · see citing works
Morchid, A., Et-taibi, B., Oughannou, Z., Alami, R. E., Qjidaa, H., Jamil, M. O., Boufounas, E., & Abid, M. R. (2024) · Scientific African · doi:10.1016/j.sciaf.2024.e02527 · Cited by 92 · see citing works
Machine learning for microalgae detection and utilization
Ning, H., Li, R., & Zhou, T. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.947394 · Cited by 83 · see citing works
Morchid, A., Said, Z., Abdelaziz, A. Y., et al. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.100979 · Cited by 75 · see citing works
Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks
Esmaeili, F., Cassie, E., Nguyen, H. P. T., Plank, N. O. V., Unsworth, C. P., & Wang, A. (2023) · Bioengineering · doi:10.3390/bioengineering10040405 · Cited by 74 · see citing works
Botryococcus braunii strains compared for biomass productivity, hydrocarbon and carbohydrate content
Gouveia, J., Ruiz, J., van den Broek, L. A. M., Hesselink, T., Peters, S., Kleinegris, D. M. M., Smith, A. G., van der Veen, D., Barbosa, M. J., & Wijffels, R. H. (2017) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2017.03.008 · Cited by 73 · see citing works
LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor
Lachekhab, F., Benzaoui, M., Tadjer, S. A., et al. (2024) · Energies · doi:10.3390/en17102340 · Cited by 73 · see citing works
Ensemble models based on CNN and LSTM for dropout prediction in MOOC
Talebi, K., Torabi, Z., & Daneshpour, N. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121187 · Cited by 68 · see citing works
LoRa-based Network for Water Quality Monitoring in Coastal Areas
Sendra, S., Parra, L., Jimenez, J. M., et al. (2022) · Mobile Networks and Applications · doi:10.1007/s11036-022-01994-8 · Cited by 63 · see citing works
Integrated internet of things (IoT) solutions for early fire detection in smart agriculture
Morchid, A., Oughannou, Z., Alami, R. E., et al. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103392 · Cited by 63 · see citing works
Alamoodi, A. H., Zaidan, B. B., Zaidan, A. A., Albahri, O. S., Chen, J., Chyad, M. A., Garfan, S., & Aleesa, A. M. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111236 · Cited by 57 · see citing works
Yeh, Y.-C., Syed, T., Brinitzer, G., Frick, K., Schmid-Staiger, U., Haasdonk, B., Tovar, G. E. M., Krujatz, F., Mädler, J., & Urbas, L. (2023) · Bioresource Technology · doi:10.1016/j.biortech.2023.129882 · Cited by 56 · see citing works
Rostam, N. A. P., Malim, N. H. A. H., Abdullah, R., Ahmad, A. L., Ooi, B. S., & Chan, D. J. C. (2021) · IEEE Access · doi:10.1109/access.2021.3102044 · Cited by 55 · see citing works
Application of machine learning in river water quality management: a review
Cojbasic, S., Dmitrašinović, S., Kostic, M., et al. (2023) · Water Science & Technology · doi:10.2166/wst.2023.331 · Cited by 50 · see citing works
Hydrocarbon production in high density Botryococcus braunii race B continuous culture
Khatri, W., Hendrix, R., Niehaus, T. D., Chappell, J., & Curtis, W. R. (2013) · Biotechnology and Bioengineering · doi:10.1002/bit.25126 · Cited by 49 · see citing works
Song, L., Qin, J. G., Su, S., Xu, J., Clarke, S., & Shan, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041459 · Cited by 48 · see citing works
Han, W., Jin, W., Ding, W., Shu-ping, L., Song, K., Chen, C., Qin, C., Chen, Y., Tu, R., & Zhou, X. (2021) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2021.106491 · Cited by 48 · see citing works
Machine learning-based optimisation of microalgae biomass production by using wastewater
Singh, V., Verma, M., Chivate, M. S., & Mishra, V. (2023) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2023.111387 · Cited by 44 · see citing works
Insights of microalgae-based aquaculture feed: A review on circular bioeconomy and perspectives
Tham, P. E., Lim, H. R., Khoo, K. S., Chew, K. W., Yap, Y. J., Munawaroh, H. S. H., Ma, Z., Rajendran, S., Gnanasekaran, L., & Show, P.-L. (2023) · Algal Research · doi:10.1016/j.algal.2023.103186 · Cited by 43 · see citing works
Joshi, N., Park, J., Zhao, K., Londo, A., & Khanal, S. (2024) · Remote Sensing · doi:10.3390/rs16132444 · Cited by 39 · see citing works
(2015) · Joint Research Centre (European Commission) · doi:10.2790/125847 · Cited by 37 · see citing works
Li, L., Li, X., Hong, Y., Jiang, M.-R., & Lu, S.-L. (2020) · Algal Research · doi:10.1016/j.algal.2020.101851 · Cited by 36 · see citing works
An investigation into producing Botryococcus braunii in a tubular photobioreactor
Mora, L. S. P., Matsudo, M. C., Cezare-Gomes, E. A., & de Carvalho, J. C. M. (2016) · Journal of Chemical Technology & Biotechnology · doi:10.1002/jctb.4934 · Cited by 32 · see citing works
Meenatchisundaram, K., Gowd, S. C., Lee, J., Barathi, S., & Rajendran, K. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.103670 · Cited by 29 · see citing works
Zhang, D., Chen, B., Hubacek, K., Meng, J., Sun, M., Mao, J., Jin, M., Almeida, C. M. V. B., Chiu, A. S. F., Yang, L., et al. (2023) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2023.106985 · Cited by 23 · see citing works
Huang, J., & Sun, H. (2016) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs.2016.20 · Cited by 20 · see citing works
The Preliminary Design of Water Quality Monitor System for the Ecological Pond based on LoRaWAN
Hsieh, C.-W., Tsai, Y. J., Stefanie, C., et al. (2020) · International Symposium on Computer, Consumer and Control · doi:10.1109/is3c50286.2020.00100 · Cited by 16 · see citing works
Gani, P., Apandi, N. M., Sunar, N. M., Matias‐Peralta, H. M., Hua, A. K., Apandi, A. M., Nagarajah, R., & Dzulkifli, S. N. M. (2022) · Biofuels · doi:10.1080/17597269.2022.2147139 · Cited by 9 · see citing works
Potential microbes in bioremediation: A review
Bhowmick, K., Roy, D., Rana, D. K., et al. (2024) · Materials Today Sustainability · doi:10.1016/j.mtsust.2024.101032 · Cited by 7 · see citing works
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Research works printed but not linked: 2
- Ahmed U, Nazir M, Sarwar A, Ali T, Aggoune E-HM, Shahzad T et al (2025) Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering. Sci Rep 15(1):1726
- Ahn H, Yeo I (2021) Deep-learning-based approach to anomaly detection techniques for large acoustic data in machine operation. Sensors (Basel) 21(16):5446
Cited by
Sorted by citations
Microalgae biotechnology and its role in sustainable and healthy food design
Schneider, A. T., Machado, R. L. S., Dutra, D. A., Machado, E. F., Dias, R. R., Deprá, M. C., Zepka, L. Q., & Jacob-Lopes, E. (2025) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1716473 · Cited by 15 · see citing works
- Authors
- Soon, C. F., Jamia’an, M., Mohamed Sunar, N., Arifin, S. N. H., Tay, K. G., Heng, C., See, C. H., Mat Nayan, N. H., & Tee, K. S.
- Affiliations
- Tun Hussein Onn University of Malaysia (MY)
- Curtin University Sarawak (MY)
- Edinburgh Napier University (GB)
- Corresponding author
- Soon, Chin Fhong
- ORCID
- Venue
- Aquaculture International
- Published
- 2025-07-11
- Volume
- 33
- Issue
- 6
- ISSN
- 0967-6120 (ISSN-L)
- 1573-143X
- OpenAlex
- W4412185167
- Cited by
- 16
- References
- 50
- Citations by year
- 2026: 12 · 2025: 4
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Aquaculture International (publishedVersion, cc-by-nc-nd)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (acceptedVersion, cc-by-nc-nd)
- Funders
- Universiti Tun Hussein Onn Malaysia
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Life Sciences › Agricultural and Biological Sciences › Aquatic Science › Innovations in Aquaponics and Hydroponics Systems
- Keywords
- microalgae cultivation, anomaly detection, LSTM autoencoder, Internet of Things, redox potential, water quality monitoring, solar-powered sensors, reconstruction error, moving average filter, smart aquaculture
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