Research Article
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Year 2024, Volume: 9 Issue: 2, 234 - 243, 30.10.2024
https://doi.org/10.28978/nesciences.1575456

Abstract

References

  • Akhter, F., Siddiquei, H. R., Alahi, M. E. E., & Mukhopadhyay, S. C. (2021). Recent advancement of the sensors for monitoring the water quality parameters in smart fisheries farming. Computers, 10(3), 26. https://doi.org/10.3390/computers10030026.
  • Aura, C. M., Musa, S., Yongo, E., Okechi, J. K., Njiru, J. M., Ogari, Z., & Oucho, J. A. (2018). Integration of mapping and socio‐economic status of cage culture: Towards balancing lake‐use and culture fisheries in Lake Victoria, Kenya. Aquaculture Research, 49(1), 532-545.
  • Becke, C., Schumann, M., Steinhagen, D., Rojas-Tirado, P., Geist, J., & Brinker, A. (2019). Effects of unionized ammonia and suspended solids on rainbow trout (Oncorhynchus mykiss) in recirculating aquaculture systems. Aquaculture, 499, 348-357.
  • Bobir, A.O., Askariy, M., Otabek, Y.Y., Nodir, R.K., Rakhima, A., Zukhra, Z.Y., Sherzod, A.A. (2024). Utilizing Deep Learning and the Internet of Things to Monitor the Health of Aquatic Ecosystems to Conserve Biodiversity. Natural and Engineering Sciences, 9(1), 72-83.
  • Bolfe, É. L., Jorge, L. A. D. C., Sanches, I. D. A., Luchiari Júnior, A., da Costa, C. C., Victoria, D. D. C., & Ramirez, A. R. (2020). Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture, 10(12), 653. https://doi.org/10.3390/agriculture10120653.
  • Boonsong, W. A. S. A. N. A., Ismail, W., Shinohara, N. A. O. K. I., Nameh, S. M. I. S., Alifah, S. U. R. Y. A. N. I., Hafiz, K. A. M. A. R. U. L., & Kamaludin, T. A. (2020). Real-time water quality monitoring of aquaculture pond using wireless sensor network and internet of things. Journal of Theoretical and Applied Information Technology, 98(22), 3573-3582.
  • Buljubašić, S. (2020). Application of New Technologies in the Water Supply System. Archives for Technical Sciences, 1(22), 27–34.
  • Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745.
  • Giacomazzo, M., Bertolo, A., Brodeur, P., Massicotte, P., Goyette, J. O., & Magnan, P. (2020). Linking fisheries to land use: How anthropogenic inputs from the watershed shape fish habitat quality. Science of The Total Environment, 717, 135377. https://doi.org/10.1016/j.scitotenv.2019.135377
  • Grilli, G., Mukhopadhyay, S., Curtis, J., & Hynes, S. (2020). Recreational angling demand in a mixed resource fishery. Fisheries Management and Ecology, 27(6), 591-599.
  • Jayapriya, R. (2021). Scientometrics Analysis on Water Treatment During 2011 to 2020. Indian Journal of Information Sources and Services, 11(2), 58–63.
  • Kang, J., Kim, J., & Sohn, M. M. (2019). Supervised learning-based Lifetime Extension of Wireless Sensor Network Nodes. Journal of Internet Services and Information Security, 9(4), 59-67.
  • Lindholm‐Lehto, P. (2023). Water quality monitoring in recirculating aquaculture systems. Aquaculture, Fish and Fisheries, 3(2), 113-131.
  • Lindholm-Lehto, P. C., Suurnäkki, S., Pulkkinen, J. T., Aalto, S. L., Tiirola, M., & Vielma, J. (2019). Effect of peracetic acid on levels of geosmin, 2-methylisoborneol, and their potential producers in a recirculating aquaculture system for rearing rainbow trout (Oncorhynchus mykiss). Aquacultural Engineering, 85, 56-64.
  • MacNeil, M. A., Mellin, C., Matthews, S., Wolff, N. H., McClanahan, T. R., Devlin, M., & Graham, N. A. (2019). Water quality mediates resilience on the Great Barrier Reef. Nature Ecology & Evolution, 3(4), 620-627.
  • Naughton, S., Kavanagh, S., Lynch, M., & Rowan, N. J. (2020). Synchronizing use of sophisticated wet-laboratory and in-field handheld technologies for real-time monitoring of key microalgae, bacteria and physicochemical parameters influencing efficacy of water quality in a freshwater aquaculture recirculation system: A case study from the Republic of Ireland. Aquaculture, 526, 735377. https://doi.org/10.1016/j.aquaculture.2020.735377.
  • Robles, T., Alcarria, R., De Andrés, D.M., De la Cruz, M.N., Calero, R., Iglesias, S., & Lopez, M. (2015). An IoT based reference architecture for smart water management processes. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 6(1), 4-23.
  • Rojas-Tirado, P., Pedersen, P. B., Vadstein, O., & Pedersen, L. F. (2018). Changes in microbial water quality in RAS following altered feed loading. Aquacultural engineering, 81, 80-88.
  • Silva, G. M. E., Campos, D. F., Brasil, J. A. T., Tremblay, M., Mendiondo, E. M., & Ghiglieno, F. (2022). Advances in technological research for online and in situ water quality monitoring—A review. Sustainability, 14(9), 5059. https://doi.org/10.3390/su14095059.
  • Su, X., Sutarlie, L., & Loh, X. J. (2020). Sensors, biosensors, and analytical technologies for aquaculture water quality. Research. https://doi.org/10.34133/2020/8272705
  • Suurnäkki, S., Pulkkinen, J. T., Lindholm-Lehto, P. C., Tiirola, M., & Aalto, S. L. (2020). The effect of peracetic acid on microbial community, water quality, nitrification and rainbow trout (Oncorhynchus mykiss) performance in recirculating aquaculture systems. Aquaculture, 516, 734534.
  • Tolentino, L. K. S., De Pedro, C. P., Icamina, J. D., Navarro, J. B. E., Salvacion, L. J. D., Sobrevilla, G. C. D., & Madrigal, G. A. M. (2020). Development of an IoT-based intensive aquaculture monitoring system with automatic water correction. International Journal of Computing and Digital Systems, 10, 1355-1365.
  • Xiao, R., Wei, Y., An, D., Li, D., Ta, X., Wu, Y., & Ren, Q. (2019). A review on the research status and development trend of equipment in water treatment processes of recirculating aquaculture systems. Reviews in Aquaculture, 11(3), 863-895.

Effective Surveillance of Water Quality in Recirculating Aquaculture Systems through the Application of Intelligent Biosensors

Year 2024, Volume: 9 Issue: 2, 234 - 243, 30.10.2024
https://doi.org/10.28978/nesciences.1575456

Abstract

Water quality (WQ) is the paramount element influencing fish well-being and productivity in aquaculture farming systems. The survival of fish is mostly reliant on the aquatic environment that sustains them. Consequently, it is vital to possess a comprehensive awareness of the WQ prerequisites for the fish. Optimal WQ in Recirculating Aquaculture Systems (RAS) is essential for cultivating species' effective development and survival. Currently, no laws dictate the parameters to be monitored in RAS, leaving each farmer to choose which parameters to monitor. Historically, WQ measurements have been assessed at certain intervals using portable sensors and laboratory tests, which may be labor-intensive. This study proposes an Effective Surveillance of Water Quality (ESWQ) in RAS using Intelligent Biosensors (IBS). This study examines essential water characteristics (temperature, pH, calcium, magnesium, and Dissolved Oxygen (DO)) for RAS and evaluates the IBS for monitoring these factors. This research provides a potential solution for RAS using IBS, which would enhance ESWQ aspects, facilitate data-driven decision-making, and enable more rapid adaptation to evolving RAS situations.

References

  • Akhter, F., Siddiquei, H. R., Alahi, M. E. E., & Mukhopadhyay, S. C. (2021). Recent advancement of the sensors for monitoring the water quality parameters in smart fisheries farming. Computers, 10(3), 26. https://doi.org/10.3390/computers10030026.
  • Aura, C. M., Musa, S., Yongo, E., Okechi, J. K., Njiru, J. M., Ogari, Z., & Oucho, J. A. (2018). Integration of mapping and socio‐economic status of cage culture: Towards balancing lake‐use and culture fisheries in Lake Victoria, Kenya. Aquaculture Research, 49(1), 532-545.
  • Becke, C., Schumann, M., Steinhagen, D., Rojas-Tirado, P., Geist, J., & Brinker, A. (2019). Effects of unionized ammonia and suspended solids on rainbow trout (Oncorhynchus mykiss) in recirculating aquaculture systems. Aquaculture, 499, 348-357.
  • Bobir, A.O., Askariy, M., Otabek, Y.Y., Nodir, R.K., Rakhima, A., Zukhra, Z.Y., Sherzod, A.A. (2024). Utilizing Deep Learning and the Internet of Things to Monitor the Health of Aquatic Ecosystems to Conserve Biodiversity. Natural and Engineering Sciences, 9(1), 72-83.
  • Bolfe, É. L., Jorge, L. A. D. C., Sanches, I. D. A., Luchiari Júnior, A., da Costa, C. C., Victoria, D. D. C., & Ramirez, A. R. (2020). Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture, 10(12), 653. https://doi.org/10.3390/agriculture10120653.
  • Boonsong, W. A. S. A. N. A., Ismail, W., Shinohara, N. A. O. K. I., Nameh, S. M. I. S., Alifah, S. U. R. Y. A. N. I., Hafiz, K. A. M. A. R. U. L., & Kamaludin, T. A. (2020). Real-time water quality monitoring of aquaculture pond using wireless sensor network and internet of things. Journal of Theoretical and Applied Information Technology, 98(22), 3573-3582.
  • Buljubašić, S. (2020). Application of New Technologies in the Water Supply System. Archives for Technical Sciences, 1(22), 27–34.
  • Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of Things (IoT)-based sustainable agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745.
  • Giacomazzo, M., Bertolo, A., Brodeur, P., Massicotte, P., Goyette, J. O., & Magnan, P. (2020). Linking fisheries to land use: How anthropogenic inputs from the watershed shape fish habitat quality. Science of The Total Environment, 717, 135377. https://doi.org/10.1016/j.scitotenv.2019.135377
  • Grilli, G., Mukhopadhyay, S., Curtis, J., & Hynes, S. (2020). Recreational angling demand in a mixed resource fishery. Fisheries Management and Ecology, 27(6), 591-599.
  • Jayapriya, R. (2021). Scientometrics Analysis on Water Treatment During 2011 to 2020. Indian Journal of Information Sources and Services, 11(2), 58–63.
  • Kang, J., Kim, J., & Sohn, M. M. (2019). Supervised learning-based Lifetime Extension of Wireless Sensor Network Nodes. Journal of Internet Services and Information Security, 9(4), 59-67.
  • Lindholm‐Lehto, P. (2023). Water quality monitoring in recirculating aquaculture systems. Aquaculture, Fish and Fisheries, 3(2), 113-131.
  • Lindholm-Lehto, P. C., Suurnäkki, S., Pulkkinen, J. T., Aalto, S. L., Tiirola, M., & Vielma, J. (2019). Effect of peracetic acid on levels of geosmin, 2-methylisoborneol, and their potential producers in a recirculating aquaculture system for rearing rainbow trout (Oncorhynchus mykiss). Aquacultural Engineering, 85, 56-64.
  • MacNeil, M. A., Mellin, C., Matthews, S., Wolff, N. H., McClanahan, T. R., Devlin, M., & Graham, N. A. (2019). Water quality mediates resilience on the Great Barrier Reef. Nature Ecology & Evolution, 3(4), 620-627.
  • Naughton, S., Kavanagh, S., Lynch, M., & Rowan, N. J. (2020). Synchronizing use of sophisticated wet-laboratory and in-field handheld technologies for real-time monitoring of key microalgae, bacteria and physicochemical parameters influencing efficacy of water quality in a freshwater aquaculture recirculation system: A case study from the Republic of Ireland. Aquaculture, 526, 735377. https://doi.org/10.1016/j.aquaculture.2020.735377.
  • Robles, T., Alcarria, R., De Andrés, D.M., De la Cruz, M.N., Calero, R., Iglesias, S., & Lopez, M. (2015). An IoT based reference architecture for smart water management processes. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 6(1), 4-23.
  • Rojas-Tirado, P., Pedersen, P. B., Vadstein, O., & Pedersen, L. F. (2018). Changes in microbial water quality in RAS following altered feed loading. Aquacultural engineering, 81, 80-88.
  • Silva, G. M. E., Campos, D. F., Brasil, J. A. T., Tremblay, M., Mendiondo, E. M., & Ghiglieno, F. (2022). Advances in technological research for online and in situ water quality monitoring—A review. Sustainability, 14(9), 5059. https://doi.org/10.3390/su14095059.
  • Su, X., Sutarlie, L., & Loh, X. J. (2020). Sensors, biosensors, and analytical technologies for aquaculture water quality. Research. https://doi.org/10.34133/2020/8272705
  • Suurnäkki, S., Pulkkinen, J. T., Lindholm-Lehto, P. C., Tiirola, M., & Aalto, S. L. (2020). The effect of peracetic acid on microbial community, water quality, nitrification and rainbow trout (Oncorhynchus mykiss) performance in recirculating aquaculture systems. Aquaculture, 516, 734534.
  • Tolentino, L. K. S., De Pedro, C. P., Icamina, J. D., Navarro, J. B. E., Salvacion, L. J. D., Sobrevilla, G. C. D., & Madrigal, G. A. M. (2020). Development of an IoT-based intensive aquaculture monitoring system with automatic water correction. International Journal of Computing and Digital Systems, 10, 1355-1365.
  • Xiao, R., Wei, Y., An, D., Li, D., Ta, X., Wu, Y., & Ren, Q. (2019). A review on the research status and development trend of equipment in water treatment processes of recirculating aquaculture systems. Reviews in Aquaculture, 11(3), 863-895.
There are 23 citations in total.

Details

Primary Language English
Subjects Agricultural Biotechnology (Other)
Journal Section Articles
Authors

Manish Nandy 0009-0003-7578-3505

Ahilya Dubey This is me 0009-0008-1681-8823

Publication Date October 30, 2024
Submission Date October 29, 2024
Acceptance Date October 30, 2024
Published in Issue Year 2024 Volume: 9 Issue: 2

Cite

APA Nandy, M., & Dubey, A. (2024). Effective Surveillance of Water Quality in Recirculating Aquaculture Systems through the Application of Intelligent Biosensors. Natural and Engineering Sciences, 9(2), 234-243. https://doi.org/10.28978/nesciences.1575456

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