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Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions

Year 2025, Volume: 11 Issue: 2, 172 - 181, 26.06.2025
https://doi.org/10.58626/memba.1728306

Abstract

Artificial Intelligence (AI) is becoming increasingly important to increase efficiency and sustainability by organizing processes and providing significant improvements in different sectors. AI is reshaping the aquaculture sector. AI is being used to help solve optimization problems in aquaculture, reduce costs, and find sustainable solutions. These scenarios guided the evaluation of the future role of AI in aquaculture and the benefits that AI brings to the aquaculture sector in this study. AI is improving the way fish stocks are monitored, live stocks are assessed, diseases among plants and animals are recognized, water quality is tested, and production lines are controlled. Aquaculture uses AI technologies to help detect diseases early, monitor environmental conditions affecting production, and improve both efficiency and sustainability. In addition, AI is examining and providing solutions to environmental issues such as global warming and water resource management.

References

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  • Ahmad, T., Ali, L., Alshamsi, D., Aldahan, A., El-Askary, H., & Ahmed, A. (2024). AI-Powered Water Quality Index Prediction: Unveiling Machine Learning Precision in Hyper-Arid Regions. Earth Systems and Environment, 1-18.
  • Ahmed, I., Jeon, G., & Piccialli, F. (2022). From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Transactions on Industrial Informatics, 18(8): 5031-5042.
  • Alam, M., Khan, I. R., Siddiqui, F., & Alam, M. A. (2024). Artificial Intelligence as Key Enabler for Safeguarding the Marine Resources. In Artificial Intelligence and Edge Computing for Sustainable Ocean Health, 409-451.
  • Anwar, H., Anwar, T., & Mahmood, G. (2023). Nourishing the Future: AI-Driven Optimization of Farm-to-Consumer Food Supply Chain for Enhanced Business Performance. Innovative Computing Review, 3(2).
  • Aragonés Lozano, M., Pérez Llopis, I., & Esteve Domingo, M. (2023). Threat hunting architecture using a machine learning approach for critical infrastructures protection. Big data and cognitive computing, 7(2).
  • Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19, 100330.
  • Bohara, K., Joshi, P., Acharya, K. P., & Ramena, G. (2024). Emerging technologies revolutionising disease diagnosis and monitoring in aquatic animal health. Reviews in Aquaculture, 16(2): 836-854.
  • Brown, A. R., Wilson, R. W., & Tyler, C. R. (2024). Assessing the Benefits and Challenges of Recirculating Aquaculture Systems (RAS) for Atlantic Salmon Production. Reviews in Fisheries Science & Aquaculture, 1-22.
  • Capetillo-Contreras, O., Pérez-Reynoso, F. D., Zamora-Antuñano, M. A., Álvarez-Alvarado, J. M., & Rodríguez-Reséndiz, J. (2024). Artificial intelligence-based aquaculture system for optimizing the quality of water: A systematic analysis. Journal of Marine Science and Engineering, 12(1).
  • Congdon, J. V., Hosseini, M., Gading, E. F., Masousi, M., Franke, M., & MacDonald, S. E. (2022). The future of artificial intelligence in monitoring animal identification, health, and behaviour. Animals, 12(13).
  • Cooke, S. J., Auld, H. L., Birnie-Gauvin, K., Elvidge, C. K., Piczak, M. L., Twardek, W. M., & Muir, A. M. (2023). On the relevance of animal behavior to the management and conservation of fishes and fisheries. Environmental Biology of Fishes, 106(5): 785-810.
  • Dewali, S., Sharma, N., Melkani, D., Arya, M., Kathayat, N., Panda, A. K., & Bisht, S. S. (2023). Aquaculture: Contributions to global food security. In Emerging solutions in sustainable food and nutrition security (pp. 123-139). Cham: Springer International Publishing.
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  • Ditria, E. M., Buelow, C. A., Gonzalez-Rivero, M., & Connolly, R. M. (2022). Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective. Frontiers in Marine Science, 9.
  • Dongyu, Q. (2024). 2024 THE STATE OF WORLD FISHERIES AND AQUACULTURE-BLUE TRANSFORMATION IN ACTION. The State of World Fisheries and Aquaculture, R1-232.
  • Elufioye, O. A., Ike, C. U., Odeyemi, O., Usman, F. O., & Mhlongo, N. Z. (2024). Ai-Driven predictive analytics in agricultural supply chains: a review: assessing the benefits and challenges of ai in forecasting demand and optimizing supply in agriculture. Computer Science & IT Research Journal, 5(2): 473-497.
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  • Faichuk, O., Voliak, L., Hutsol, T., Glowacki, S., Pantsyr, Y., Slobodian, S., & Gródek-Szostak, Z. (2022). European Green Deal: Threats Assessment for Agri-Food Exporting Countries to the EU. Sustainability, 14(7), 3712.
  • Fu, X., Zhang, C., Chang, F., Han, L., Zhao, X., Wang, Z., & Ma, Q. (2024). Simulation and forecasting of fishery weather based on statistical machine learning. Information Processing in Agriculture, 11(1), 127-142.servation of marine ecosystems: A perspective. Frontiers in Marine Science, 9.
  • Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67.
  • Gambín, Á. F., Angelats, E., González, J. S., Miozzo, M., & Dini, P. (2021). Sustainable marine ecosystems: Deep learning for water quality assessment and forecasting. IEEE access, 9: 121344-121365.
  • Gebremedhin, S., Bruneel, S., Getahun, A., Anteneh, W., & Goethals, P. (2021). Scientific methods to understand fish population dynamics and support sustainable fisheries management. Water, 13(4).
  • Gladju, J., Kamalam, B. S., & Kanagaraj, A. (2022). Applications of data mining and machine learning framework in aquaculture and fisheries: A review. Smart Agricultural Technology, 2(100061).
  • Goodwin, M., Halvorsen, K. T., Jiao, L., Knausgård, K. M., Martin, A. H., Moyano, M., ... & Thorbjørnsen, S. H. (2022). Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook. ICES Journal of Marine Science, 79(2): 319-336.
  • Grewal, D., Guha, A., Noble, S. M., & Bentley, K. (2024). The food production–consumption chain: Fighting food insecurity, loss, and waste with technology. Journal of the Academy of Marketing Science, 52(5): 1412-1430.
  • Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25: 1315-1360.
  • Hanoon, M. S., Ahmed, A. N., Fai, C. M., Birima, A. H., Razzaq, A., Sherif, M., & El-Shafie, A. (2021). Application of artificial intelligence models for modeling water quality in groundwater: comprehensive review, evaluation and future trends. Water, Air, & Soil Pollution, 232: 1-41.
  • Hatzilygeroudis, I., Dimitropoulos, K., Kovas, K., & Theodorou, J. A. (2023). Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal. Journal of Marine Science and Engineering, 11(5).
  • Honarmand Ebrahimi, S., Ossewaarde, M., & Need, A. (2021). Smart fishery: a systematic review and research agenda for sustainable fisheries in the age of AI. Sustainability, 13(11).
  • Islam, S. I., Ahammad, F., & Mohammed, H. (2024). Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges. Journal of the World Aquaculture Society, 55(2).
  • Krishnan, S. R., Nallakaruppan, M. K., Chengoden, R., Koppu, S., Iyapparaja, M., Sadhasivam, J., & Sethuraman, S. (2022). Smart water resource management using Artificial Intelligence—A review. Sustainability, 14(20).
  • Kumar, S., Srivastava, A., & Maity, R. (2024). Modeling climate change impacts on vector-borne disease using machine learning models: Case study of Visceral leishmaniasis (Kala-azar) from Indian state of Bihar. Expert Systems with Applications, 237(121490).
  • Mandal, A., & Ghosh, A. R. (2024). Role of artificial intelligence (AI) in fish growth and health status monitoring: A review on sustainable aquaculture. Aquaculture International, 32(3): 2791-2820.
  • Messina, M. (2022). Perspective: Soybeans can help address the caloric and protein needs of a growing global population. Frontiers in nutrition, 9, 909464.
  • Mohale, H. P., Narsale, S. A., Kadam, R. V., Prakash, P., Sheikh, S., Mansukhbhai, C. R., & Baraiya, R. (2024). Artificial Intelligence in Fisheries and Aquaculture: Enhancing Sustainability and Productivity. Archives of Current Research International, 24(3): 106-123.
  • Mugwanya, M., Dawood, M. A., Kimera, F., & Sewilam, H. (2022). Anthropogenic temperature fluctuations and their effect on aquaculture: A comprehensive review. Aquaculture and Fisheries, 7(3): 223-243.
  • Mustapha, U. F., Alhassan, A. W., Jiang, D. N., & Li, G. L. (2021). Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA). Reviews in Aquaculture, 13(4): 2076-2091.
  • Nagothu, S. K., Bindu Sri, P., Anitha, G., Vincent, S., & Kumar, O. P. (2025). Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture. Aquaculture International, 33(1).
  • Pachaiappan, R., Cornejo-Ponce, L., Rajendran, R., Manavalan, K., Femilaa Rajan, V., & Awad, F. (2022). A review on biofiltration techniques: recent advancements in the removal of volatile organic compounds and heavy metals in the treatment of polluted water. Bioengineered, 13(4), 8432-8477.
  • Pajic, V., Andrejic, M., & Chatterjee, P. (2024). Enhancing cold chain logistics: A framework for advanced temperature monitoring in transportation and storage. Mechatron. Intell Transp. Syst, 3(1): 16-30.
  • Parab, V., Prajapati, J. J., Karan, S., Bhowmick, A. R., & Mukherjee, J. (2023). Impact of abiotic factors and heavy metals in predicting the population decline of Near Threatened fish Notopterus chitala in natural habitat. Aquatic Ecology, 57(4): 863-879.
  • Piñeros, V. J., Reveles-Espinoza, A. M., & Monroy, J. A. (2024). From Remote Sensing to Artificial Intelligence in Coral Reef Monitoring. Machines, 12(10).
  • Prapti, D. R., Mohamed Shariff, A. R., Che Man, H., Ramli, N. M., Perumal, T., & Shariff, M. (2022). Internet of Things (IoT)‐based aquaculture: An overview of IoT application on water quality monitoring. Reviews in Aquaculture, 14(2), 979-992.
  • Rakkannan, G., & Agarwal, D. (2025). Role of Aquaculture Biotechnology in Food Security and Nutrition. In Food Security, Nutrition and Sustainability Through Aquaculture Technologies (pp. 173-191). Cham: Springer Nature Switzerland.
  • Rashid, A. B., & Kausik, A. K. (2024). AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Advances, 100277.
  • Rodriguez, P., & Costa, I. (2024). Exploring the Role of AI in Sustainable Development and Environmental Monitoring. MZ Computing Journal, 5(1).
  • Shitu, A., Liu, G., Muhammad, A. I., Zhang, Y., Tadda, M. A., Qi, W., & Zhu, S. (2022). Recent advances in application of moving bed bioreactors for wastewater treatment from recirculating aquaculture systems: A review. Aquaculture and Fisheries, 7(3), 244-258.
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Su Ürünleri Endüstrisinde Yapay Zekâ Uygulamaları: Sürdürülebilirlik, Verimlilik ve Yenilikçi Çözümler

Year 2025, Volume: 11 Issue: 2, 172 - 181, 26.06.2025
https://doi.org/10.58626/memba.1728306

Abstract

Yapay Zekâ (YZ), süreçleri organize ederek ve farklı sektörlerde önemli gelişmeler sağlayarak verimliliği ve sürdürülebilirliği artırmak için giderek önemli bir hale gelmektedir. YZ, su ürünleri yetiştiriciliği sektörünü yeniden şekillendirmektedir. YZ, su ürünleri yetiştiriciliğinde optimizasyonla ilgili sorunları çözmeye, maliyetleri düşürmeye ve sürdürülebilir çözümler bulmaya yardımcı olmak için kullanılmaktadır. Bu senaryolar, bu çalışma kapsamında YZ'nın su ürünleri yetiştiriciliğindeki gelecekteki rolünün ve YZ'nın su ürünleri yetiştiriciliği sektörüne sağladığı faydaların değerlendirilmesine rehberlik etmiştir. YZ, balık stoklarının izlenmesi, canlı stokların değerlendirilmesi, bitkiler ve hayvanlar arasındaki hastalıkların tanınması, su kalitesinin test edilmesi ve üretim hatlarının kontrol edilmesi şeklini geliştirmektedir. Hastalıkları erken tespit etmeye, üretimi etkileyen çevre koşullarını izlemeye ve hem verimliliği hem de sürdürülebilirliği iyileştirmeye yardımcı olmak için su ürünleri yetiştiriciliği YZ teknolojilerini kullanmaktadır. Ayrıca, yapay zekâ küresel ısınma ve su kaynaklarının yönetimi gibi çevreyi etkileyen sorunları incelemekte ve bu konulara çözümler sunmaktadır.

References

  • Ahmad, A., Abdullah, S. R. S., Hasan, H. A., Othman, A. R., & Ismail, N. I. (2021). Aquaculture industry: Supply and demand, best practices, effluent and its current issues and treatment technology. Journal of Environmental Management, 287, 112271.
  • Ahmad, T., Ali, L., Alshamsi, D., Aldahan, A., El-Askary, H., & Ahmed, A. (2024). AI-Powered Water Quality Index Prediction: Unveiling Machine Learning Precision in Hyper-Arid Regions. Earth Systems and Environment, 1-18.
  • Ahmed, I., Jeon, G., & Piccialli, F. (2022). From artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where. IEEE Transactions on Industrial Informatics, 18(8): 5031-5042.
  • Alam, M., Khan, I. R., Siddiqui, F., & Alam, M. A. (2024). Artificial Intelligence as Key Enabler for Safeguarding the Marine Resources. In Artificial Intelligence and Edge Computing for Sustainable Ocean Health, 409-451.
  • Anwar, H., Anwar, T., & Mahmood, G. (2023). Nourishing the Future: AI-Driven Optimization of Farm-to-Consumer Food Supply Chain for Enhanced Business Performance. Innovative Computing Review, 3(2).
  • Aragonés Lozano, M., Pérez Llopis, I., & Esteve Domingo, M. (2023). Threat hunting architecture using a machine learning approach for critical infrastructures protection. Big data and cognitive computing, 7(2).
  • Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19, 100330.
  • Bohara, K., Joshi, P., Acharya, K. P., & Ramena, G. (2024). Emerging technologies revolutionising disease diagnosis and monitoring in aquatic animal health. Reviews in Aquaculture, 16(2): 836-854.
  • Brown, A. R., Wilson, R. W., & Tyler, C. R. (2024). Assessing the Benefits and Challenges of Recirculating Aquaculture Systems (RAS) for Atlantic Salmon Production. Reviews in Fisheries Science & Aquaculture, 1-22.
  • Capetillo-Contreras, O., Pérez-Reynoso, F. D., Zamora-Antuñano, M. A., Álvarez-Alvarado, J. M., & Rodríguez-Reséndiz, J. (2024). Artificial intelligence-based aquaculture system for optimizing the quality of water: A systematic analysis. Journal of Marine Science and Engineering, 12(1).
  • Congdon, J. V., Hosseini, M., Gading, E. F., Masousi, M., Franke, M., & MacDonald, S. E. (2022). The future of artificial intelligence in monitoring animal identification, health, and behaviour. Animals, 12(13).
  • Cooke, S. J., Auld, H. L., Birnie-Gauvin, K., Elvidge, C. K., Piczak, M. L., Twardek, W. M., & Muir, A. M. (2023). On the relevance of animal behavior to the management and conservation of fishes and fisheries. Environmental Biology of Fishes, 106(5): 785-810.
  • Dewali, S., Sharma, N., Melkani, D., Arya, M., Kathayat, N., Panda, A. K., & Bisht, S. S. (2023). Aquaculture: Contributions to global food security. In Emerging solutions in sustainable food and nutrition security (pp. 123-139). Cham: Springer International Publishing.
  • Dirican, S. (2024). Current Status of Floating Net Cages Aquaculture in Sivas Province (Turkey). Asian Journal of Applied Science and Technology (AJAST), 8(4), 01-11.
  • Ditria, E. M., Buelow, C. A., Gonzalez-Rivero, M., & Connolly, R. M. (2022). Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective. Frontiers in Marine Science, 9.
  • Dongyu, Q. (2024). 2024 THE STATE OF WORLD FISHERIES AND AQUACULTURE-BLUE TRANSFORMATION IN ACTION. The State of World Fisheries and Aquaculture, R1-232.
  • Elufioye, O. A., Ike, C. U., Odeyemi, O., Usman, F. O., & Mhlongo, N. Z. (2024). Ai-Driven predictive analytics in agricultural supply chains: a review: assessing the benefits and challenges of ai in forecasting demand and optimizing supply in agriculture. Computer Science & IT Research Journal, 5(2): 473-497.
  • Engle, C. R., & van Senten, J. (2022). Resilience of communities and sustainable aquaculture: governance and regulatory effects. Fishes, 7(5).
  • Faichuk, O., Voliak, L., Hutsol, T., Glowacki, S., Pantsyr, Y., Slobodian, S., & Gródek-Szostak, Z. (2022). European Green Deal: Threats Assessment for Agri-Food Exporting Countries to the EU. Sustainability, 14(7), 3712.
  • Fu, X., Zhang, C., Chang, F., Han, L., Zhao, X., Wang, Z., & Ma, Q. (2024). Simulation and forecasting of fishery weather based on statistical machine learning. Information Processing in Agriculture, 11(1), 127-142.servation of marine ecosystems: A perspective. Frontiers in Marine Science, 9.
  • Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67.
  • Gambín, Á. F., Angelats, E., González, J. S., Miozzo, M., & Dini, P. (2021). Sustainable marine ecosystems: Deep learning for water quality assessment and forecasting. IEEE access, 9: 121344-121365.
  • Gebremedhin, S., Bruneel, S., Getahun, A., Anteneh, W., & Goethals, P. (2021). Scientific methods to understand fish population dynamics and support sustainable fisheries management. Water, 13(4).
  • Gladju, J., Kamalam, B. S., & Kanagaraj, A. (2022). Applications of data mining and machine learning framework in aquaculture and fisheries: A review. Smart Agricultural Technology, 2(100061).
  • Goodwin, M., Halvorsen, K. T., Jiao, L., Knausgård, K. M., Martin, A. H., Moyano, M., ... & Thorbjørnsen, S. H. (2022). Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook. ICES Journal of Marine Science, 79(2): 319-336.
  • Grewal, D., Guha, A., Noble, S. M., & Bentley, K. (2024). The food production–consumption chain: Fighting food insecurity, loss, and waste with technology. Journal of the Academy of Marketing Science, 52(5): 1412-1430.
  • Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021). Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25: 1315-1360.
  • Hanoon, M. S., Ahmed, A. N., Fai, C. M., Birima, A. H., Razzaq, A., Sherif, M., & El-Shafie, A. (2021). Application of artificial intelligence models for modeling water quality in groundwater: comprehensive review, evaluation and future trends. Water, Air, & Soil Pollution, 232: 1-41.
  • Hatzilygeroudis, I., Dimitropoulos, K., Kovas, K., & Theodorou, J. A. (2023). Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal. Journal of Marine Science and Engineering, 11(5).
  • Honarmand Ebrahimi, S., Ossewaarde, M., & Need, A. (2021). Smart fishery: a systematic review and research agenda for sustainable fisheries in the age of AI. Sustainability, 13(11).
  • Islam, S. I., Ahammad, F., & Mohammed, H. (2024). Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges. Journal of the World Aquaculture Society, 55(2).
  • Krishnan, S. R., Nallakaruppan, M. K., Chengoden, R., Koppu, S., Iyapparaja, M., Sadhasivam, J., & Sethuraman, S. (2022). Smart water resource management using Artificial Intelligence—A review. Sustainability, 14(20).
  • Kumar, S., Srivastava, A., & Maity, R. (2024). Modeling climate change impacts on vector-borne disease using machine learning models: Case study of Visceral leishmaniasis (Kala-azar) from Indian state of Bihar. Expert Systems with Applications, 237(121490).
  • Mandal, A., & Ghosh, A. R. (2024). Role of artificial intelligence (AI) in fish growth and health status monitoring: A review on sustainable aquaculture. Aquaculture International, 32(3): 2791-2820.
  • Messina, M. (2022). Perspective: Soybeans can help address the caloric and protein needs of a growing global population. Frontiers in nutrition, 9, 909464.
  • Mohale, H. P., Narsale, S. A., Kadam, R. V., Prakash, P., Sheikh, S., Mansukhbhai, C. R., & Baraiya, R. (2024). Artificial Intelligence in Fisheries and Aquaculture: Enhancing Sustainability and Productivity. Archives of Current Research International, 24(3): 106-123.
  • Mugwanya, M., Dawood, M. A., Kimera, F., & Sewilam, H. (2022). Anthropogenic temperature fluctuations and their effect on aquaculture: A comprehensive review. Aquaculture and Fisheries, 7(3): 223-243.
  • Mustapha, U. F., Alhassan, A. W., Jiang, D. N., & Li, G. L. (2021). Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA). Reviews in Aquaculture, 13(4): 2076-2091.
  • Nagothu, S. K., Bindu Sri, P., Anitha, G., Vincent, S., & Kumar, O. P. (2025). Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture. Aquaculture International, 33(1).
  • Pachaiappan, R., Cornejo-Ponce, L., Rajendran, R., Manavalan, K., Femilaa Rajan, V., & Awad, F. (2022). A review on biofiltration techniques: recent advancements in the removal of volatile organic compounds and heavy metals in the treatment of polluted water. Bioengineered, 13(4), 8432-8477.
  • Pajic, V., Andrejic, M., & Chatterjee, P. (2024). Enhancing cold chain logistics: A framework for advanced temperature monitoring in transportation and storage. Mechatron. Intell Transp. Syst, 3(1): 16-30.
  • Parab, V., Prajapati, J. J., Karan, S., Bhowmick, A. R., & Mukherjee, J. (2023). Impact of abiotic factors and heavy metals in predicting the population decline of Near Threatened fish Notopterus chitala in natural habitat. Aquatic Ecology, 57(4): 863-879.
  • Piñeros, V. J., Reveles-Espinoza, A. M., & Monroy, J. A. (2024). From Remote Sensing to Artificial Intelligence in Coral Reef Monitoring. Machines, 12(10).
  • Prapti, D. R., Mohamed Shariff, A. R., Che Man, H., Ramli, N. M., Perumal, T., & Shariff, M. (2022). Internet of Things (IoT)‐based aquaculture: An overview of IoT application on water quality monitoring. Reviews in Aquaculture, 14(2), 979-992.
  • Rakkannan, G., & Agarwal, D. (2025). Role of Aquaculture Biotechnology in Food Security and Nutrition. In Food Security, Nutrition and Sustainability Through Aquaculture Technologies (pp. 173-191). Cham: Springer Nature Switzerland.
  • Rashid, A. B., & Kausik, A. K. (2024). AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Advances, 100277.
  • Rodriguez, P., & Costa, I. (2024). Exploring the Role of AI in Sustainable Development and Environmental Monitoring. MZ Computing Journal, 5(1).
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There are 56 citations in total.

Details

Primary Language English
Subjects Hydrobiology, Ecology (Other), Aquaculture
Journal Section Research Articles
Authors

Esen Damla Balo Utku 0000-0003-3195-0263

Banu Kutlu 0000-0001-6348-2754

Publication Date June 26, 2025
Submission Date December 24, 2024
Acceptance Date June 3, 2025
Published in Issue Year 2025 Volume: 11 Issue: 2

Cite

APA Balo Utku, E. D., & Kutlu, B. (2025). Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions. MEMBA Su Bilimleri Dergisi, 11(2), 172-181. https://doi.org/10.58626/memba.1728306
AMA Balo Utku ED, Kutlu B. Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions. MEMBA Su Bilimleri Dergisi. June 2025;11(2):172-181. doi:10.58626/memba.1728306
Chicago Balo Utku, Esen Damla, and Banu Kutlu. “Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions”. MEMBA Su Bilimleri Dergisi 11, no. 2 (June 2025): 172-81. https://doi.org/10.58626/memba.1728306.
EndNote Balo Utku ED, Kutlu B (June 1, 2025) Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions. MEMBA Su Bilimleri Dergisi 11 2 172–181.
IEEE E. D. Balo Utku and B. Kutlu, “Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions”, MEMBA Su Bilimleri Dergisi, vol. 11, no. 2, pp. 172–181, 2025, doi: 10.58626/memba.1728306.
ISNAD Balo Utku, Esen Damla - Kutlu, Banu. “Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions”. MEMBA Su Bilimleri Dergisi 11/2 (June 2025), 172-181. https://doi.org/10.58626/memba.1728306.
JAMA Balo Utku ED, Kutlu B. Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions. MEMBA Su Bilimleri Dergisi. 2025;11:172–181.
MLA Balo Utku, Esen Damla and Banu Kutlu. “Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions”. MEMBA Su Bilimleri Dergisi, vol. 11, no. 2, 2025, pp. 172-81, doi:10.58626/memba.1728306.
Vancouver Balo Utku ED, Kutlu B. Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions. MEMBA Su Bilimleri Dergisi. 2025;11(2):172-81.

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