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The Role of Artificial Intelligence in the Fishing and Aquaculture of Bluefin Tuna (Thunnus Thynnus (Linnaeus, 1758))

Yıl 2025, Cilt: 11 Sayı: 1, 96 - 115, 28.03.2025

Öz

Artificial Intelligence (AI) refers to the development and implementation of computer systems that can perform tasks that typically require human intelligence, such as learning, problem solving, and decision making, and its use has become widespread in many sectors in recent years. AI offers opportunities for real-time monitoring, data analytics, predictive modeling, and decision support systems that can significantly improve the understanding and management of fish growth and health in aquaculture. In recent years, AI has also been used in tuna fishing and determining tuna meat quality. Companies such as TUNA SCOPE, an AI system that evaluates the quality of tuna, Cermaq, and Umitron Corporation have been making various initiatives to improve fish health and welfare. The integration of AI into aquaculture is expected to revolutionize sustainable practices by enabling data-driven decisions that increase efficiency and fish welfare while reducing labor costs and environmental impacts. The aim of our study; The aim of this study is to examine in detail the studies conducted on the use of artificial intelligence in aquaculture, the use of artificial intelligence in fisheries and aquaculture, and the use of artificial intelligence in tuna, and to create an infrastructure for future artificial intelligence applications.

Etik Beyan

Official approval is not required for this type of study.

Kaynakça

  • Austin, B., Lawrence, A., Can, E., Carboni, C., Crockett, J., Demirtas, N., Schleder, D., Adolfo, J., Kayis, S., Karacalar, U., Kizak, V., Kop, A., Thompson, K., Ruiz, C..AM., Serdar, O., Seyhaneyildiz, Can S., Watts, S., Yucel Gier, G. 2022.
  • Selected topics in sustainable aquaculture research: Current and future focus: Sustaniable Aquaculture Research. Sustain Aquat Res 1(2):74–125. https://doi.org/10.5281/zenodo.7032804.
  • Basurko, O.C., Gabina, G., Lopez, J., Granado, I., Murua, H., Fernandes, J.A., Krug, I., Ruiz, J., Uriondo, Z., 2022. Fuel consumption of free-swimming school versus FAD strategies in tropical tuna purse seine fishing. Fish. Res. 245 https://doi.org/ 10.1016/j.fishres.2021.106139.
  • Bell, J.D., Watson, R.A., Ye, Y. (2017). Global fishing capacity and fishing effort from 1950 to 2012. Fish Fish. 18(3), 489-505.
  • Block, B. A., Dewar, H., Blackwell, S. B., Williams, T. D., Prince, E. D., Farwell, C. J., Fudge, D. (2001). Migratory movements, depth preferences, and thermal biology of Atlantic bluefin tuna. Science, 293(5533), 1310-1314.
  • Chauhan, R.S., Mishra, A. 2022. New innovative technologies for sustainable aqua production. In Biodiversity. CRC Press, pp 97–111.
  • Chen, C.T., Gu, G.X. 2020. Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv Sci 7(5):1902607.
  • Chen, F., Sun, M., Du, Y., Xu, J., Zhou, L., Qiu, T., Sun, J. (2022). Intelligent feeding technique based on predicting shrimp growth in recirculating aquaculture system. Aquaculture Research, 53(12), 4401-4413.
  • Chen, J. C., Chen, T. L., Wang, H. L., Chang, P. C. (2022). Underwater abnormal classification system based on deep learning: A case study on aquaculture fish farm in Taiwan. Aquacultural Engineering, 99, 102290.
  • Claro, R., 1994. Características generales de la ictiofauna. p. 55-70. In R. Claro (ed.) Ecología de los peces marinos de Cuba. Instituto de Oceanología Academia de Ciencias de Cuba and Centro de Investigaciones de Quintana Roo.
  • Coro G., Large S., Magliozzi C., Pagano P., (2016b), Analysing and forecasting fisheries time series: purse seine in Indian Ocean as a case study, ICES Journal of Marine Science, 73, 2552-2571.
  • Coro G., Magliozzi C., Berghe E.V., Bailly N., Ellenbroek A., Pagano P., (2016a), Estimating absence locations of marine species from data of scientific surveys in OBIS, Ecological Modelling, 323, 61-76.
  • Coro G., Pagano P., Ellenbroek A., (2013a), Combining simulated expert knowledge with neural networks to produce ecological niche models for Latimeria chalumnae, Ecological Modelling, 268, 55-63.
  • Coro G., Pagano P., Ellenbroek A., (2018a), Detecting patterns of climate change in long-term forecasts of marine environmental parameters, International Journal of Digital Earth, 13, 1-19.
  • Coro G., Palma M., Ellenbroek A., Panichi G., Nair T., Pagano P., (2019), Reconstructing 3D virtual environments within a collaborative e-infrastructure, Concurrency and Computation: Practice and Experience, 31, e5028.
  • Coro G., Vilas L.G., Magliozzi C., Ellenbroek A., Scarponi P., Pagano P., (2018b), Forecasting the ongoing invasion of Lagocephalus sceleratus in the Mediterranean Sea, Ecological Modelling, 371, 37-49.
  • Coro, G. (2020). Open Science and Artificial Intelligence Supporting Blue Growth. Environmental Engineering and Management Journal, 19(10), 1719-1729.
  • Darapaneni, N., Sreekanth, S., Paduri, A. R., Roche, A. S., Murugappan, V., Singha, K. K., & Shenwai, A. V. (2022). AI based farm fish disease detection system to help micro and small fish farmers. In 2022 Interdisciplinary Research in Technology and Management (IRTM) (pp. 1-5). IEEE.
  • Dellermann, D., Ebel, P., Söllner, M., Leimeister, J.M. 2019. Hybrid intelligence. Bus Inf Syst Eng 61:637–643.
  • Desse J. and Desse-Berset¸ N 1994. Stratégies de pêche au 8ème millénaire les poissons de Cap AndreasKastros (Chypre). In: Le Brun A (eds) Fouilles récentes à Khirokitia, Editions Recherche Sur Civilisations.Vol. 3; pp. 335-360. Paris, France.
  • Erauskin-Extramiana, M., Chust, G., Arrizabalaga, H., Cheung, W.W., Santiago, J., Merino, G., Fernandes-Salvador, J.A., 2023. Implications for the global tuna fishing industry of climate change-driven alterations in productivity and body sizes. Glob. Planet. Chang. 222, 104055 https://doi.org/10.2139/ssrn.4059543.
  • FAO, (2020a), The Protected Areas Impact Maps Virtual Research Environment, i-Marine Gateway, On line at: https://imarine.d4science.org/web/protectedareaimpactmaps.
  • FAO. 2023. Using artificial intelligence to assess FAO’s knowledge base on the technology accelerator. Rome. https://doi.org/10.4060/cc6724en
  • FAO, 2024. Food And Agricultural Commodity Systems, https://www.undp.org/facs? (Erişim tarihi: 15.06.2024). Froese, R. and D. Pauly. Editors. 2024. FishBase. World Wide Web electronic publication. www.fishbase.org, version (06/2024).
  • Fromentin, J.M. and C. Ravier. 2005. The East Atlantic and Mediterranean bluefin tuna stock: looking for sustainability in a context of large uncertainties and strong political pressures. Bulletin of Marine Science Vol.76; pp. 353-362.
  • Gladjua, J., Kamalamb, B.K., Kanagara, A. 2022. Applications of data mining and machine learning framework in aquaculture and fisheries: A review, Smart Agricultural Technology, 2, 100061.
  • Goikoetxea, N., Goienetxea, I., Fernandes-Salvador, J. A., Goñi, N., Granado, I., Quincoces, I., Caballero, A. (2024). Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery. Ecological Informatics, 81, 102577.
  • Gonçalves, D. N., Acosta, P. R., Ramos, A. P. M., Osco, L. P., Furuya, D. E. G., Furuya, M. T. G., Gonçalves, W. N. (2022). Using a convolutional neural network for fingerling counting: A multi-task learning approach. Aquaculture, 557, 738334.
  • Granado, I., Hernando, L., Galparsoro, I., Gabina, ˜ G., Groba, C., Prellezo, R., Fernandes, J.A., (2021). Towards a framework for fishing route optimization decision support systems: review of the state-of-the-art and challenges. J. Clean. Prod. 320, 128661 https://doi.org/10.1016/j.jclepro.2021.128661.
  • 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), 6037. https://doi.org/10.3390/su13116037. Huang, Y. P., & Khabusi, S. P. (2025). Artificial Intelligence of Things (AIoT) Advances in Aquaculture: A Review. Processes, 13(1), 73.
  • Janiesch, C., Zschech, P., Heinrich, K. 2021. Machine learning and deep learning. Electron Mark 31(3):685–695. Kaur, R., Kumar, R., Gupta, M. 2023. Deep neural network for food image classifcation and nutrient identifcation: A systematic review. Rev Endocr Metab Disord 1–21.
  • Lee, P. G., Lea, R. N., Dohmann, E., Prebilsky, W., Turk, P. E., Ying, H., Whitson, J. L. (2000). Denitrification in aquaculture systems: an example of a fuzzy logic control problem. Aquacultural Engineering, 23(1-3), 37-59.
  • Magliozzi C., Coro G., Grabowski R.C., Packman A.I., Krause S., (2019), A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning, Environmental Modelling and Software, 111, 311-323.
  • Magliozzi, C., Coro, G., Grabowski, R. C., Packman, A. I., & Krause, S. (2019). A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning. Environmental Modelling & Software, 111, 311-323. Coro ve diğ., 2015
  • Mather F.J, Mason J.M. and Jones A.C. (1995). Historical document: life history and fisheries of Atlantic bluefin tuna. Miami: NOAA Technical Memorandum 370, USA.
  • McCauley, D. J., Woods, P., Sullivan, B., Bergman, B., Jablonicky, C., Roan, A., ... & Worm, B. (2016). Ending hide and seek at sea. Science, 351(6278), 1148-1150.
  • Munoz-Benavent, P., Andreu-García, G., Valiente-González, J. M., Atienza-Vanacloig, V., Puig-Pons, V., & Espinosa, V. (2018). Automatic Bluefin Tuna sizing using a stereoscopic vision system. ICES Journal of Marine Science, 75(1), 390-401.
  • Muñoz-Benavent, P., Martínez-Peiró, J., Andreu-García, G., Puig-Pons, V., Espinosa, V., Pérez-Arjona, I., Ortega, A. (2022). Impact evaluation of deep learning on image segmentation for automatic bluefin tuna sizing. Aquacultural Engineering, 99, 102299.
  • 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 artifcial intelligence (CIA). Rev Aquac 13(4):2076–2091.
  • O’Donncha, F., Stockwell, C.L., Planellas, S.R., Micallef, G., Palmes, P., Webb, C., Grant, J. 2021. Data driven insight into fsh behaviour and their use for precision aquaculture. Front Anim Sci 2:695054.
  • Panudju, A.T., Rahardja, S., Nurilmala, M. 2023. Decision support system in fsheries industry: Current state and future agenda. Int J Adv Sci Eng Inf Technol 13(2).
  • Parker, R. W., & Tyedmers, P. H. (2015). Fuel consumption of global fishing fleets: current understanding and knowledge gaps. Fish and Fisheries, 16(4), 684-696.
  • Rojon, I., & Smith, T. W. P. (2014). On the attitudes and opportunities of fuel consumption monitoring and measurement within the shipping industry and the identification and validation of energy efficiency and performance interventions. https://discovery.ucl.ac.uk/id/eprint/1472842/
  • Suuronen, P., Chopin, F., Glass, C., Løkkeborg, S., Matsushita, Y., Queirolo, D., & Rihan, D. (2012). Low impact and fuel efficient fishing—Looking beyond the horizon. Fisheries research, 119, 135-146.
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  • Wang, C., Li, Z., Wang, T., Xu, X., Zhang, X., Li, D. 2021. Intelligent fsh farm—The future of aquaculture. Aquacult Int 1–31.
  • Wu, Y., Duan, Y., Wei, Y., An, D., Liu, J. (2022). Application of intelligent and unmanned equipment in aquaculture: A review. Computers and Electronics in Agriculture, 199, 107201.
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Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı ve Yetiştiriciliği’nin Rolü

Yıl 2025, Cilt: 11 Sayı: 1, 96 - 115, 28.03.2025

Öz

Yapay Zeka (AI); öğrenme, problem çözme ve karar verme gibi tipik olarak insan zekası gerektiren görevleri yerine getirebilen bilgisayar sistemlerinin geliştirilmesi ve uygulanması anlamına gelmektedir ve son yıllarda birçok sektörde kullanımı yaygınlaşmıştır. Yapay zeka; balık yetiştiriciliğinde balık büyümesi ve sağlığının anlaşılmasını ve yönetimini önemli ölçüde artırabilecek gerçek zamanlı izleme, veri analitiği, tahmine dayalı modelleme ve karar destek sistemleri için fırsatlar sunmaktadır. Yapay zekanın son yıllarda orkinos avcılığı ve orkinos et kalitesinin belirlenmesinde de kullanılmaya başlandığı görülmektedir. Ton balığının kalitesini değerlendiren bir AI sistemi olan TUNA SCOPE, Cermaq ve Umitron Corporation gibi şirketlerin balık sağlığını ve refahını iyileştirmek için çeşitli girişimlerde bulundukları görülmektedir. AI'nın su ürünleri yetiştiriciliğine entegrasyonunun, işgücü maliyetlerini ve çevresel etkileri azaltırken verimliliği ve balık refahını artıran veri odaklı kararlara olanak tanıyarak sürdürülebilir uygulamalarda devrim yaratması beklenmektedir. Çalışmamızın amacı; su ürünleri yetiştiriciliğinde yapay zeka kullanımı, yapay zekanın balıkçılık ve su ürünleri yetiştiriciliğindeki kullanımı, orkinoslarda yapay zekanın kullanımı ile ilgili yapılmış çalışmaların detaylı bir şekilde incelenerek sunmak ve ileride yapılacak yapay zeka uygulamaları için bir alt yapı oluşturmaktır.

Etik Beyan

Bu tür bir çalışma için resmi onay gerekli değildir.

Kaynakça

  • Austin, B., Lawrence, A., Can, E., Carboni, C., Crockett, J., Demirtas, N., Schleder, D., Adolfo, J., Kayis, S., Karacalar, U., Kizak, V., Kop, A., Thompson, K., Ruiz, C..AM., Serdar, O., Seyhaneyildiz, Can S., Watts, S., Yucel Gier, G. 2022.
  • Selected topics in sustainable aquaculture research: Current and future focus: Sustaniable Aquaculture Research. Sustain Aquat Res 1(2):74–125. https://doi.org/10.5281/zenodo.7032804.
  • Basurko, O.C., Gabina, G., Lopez, J., Granado, I., Murua, H., Fernandes, J.A., Krug, I., Ruiz, J., Uriondo, Z., 2022. Fuel consumption of free-swimming school versus FAD strategies in tropical tuna purse seine fishing. Fish. Res. 245 https://doi.org/ 10.1016/j.fishres.2021.106139.
  • Bell, J.D., Watson, R.A., Ye, Y. (2017). Global fishing capacity and fishing effort from 1950 to 2012. Fish Fish. 18(3), 489-505.
  • Block, B. A., Dewar, H., Blackwell, S. B., Williams, T. D., Prince, E. D., Farwell, C. J., Fudge, D. (2001). Migratory movements, depth preferences, and thermal biology of Atlantic bluefin tuna. Science, 293(5533), 1310-1314.
  • Chauhan, R.S., Mishra, A. 2022. New innovative technologies for sustainable aqua production. In Biodiversity. CRC Press, pp 97–111.
  • Chen, C.T., Gu, G.X. 2020. Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv Sci 7(5):1902607.
  • Chen, F., Sun, M., Du, Y., Xu, J., Zhou, L., Qiu, T., Sun, J. (2022). Intelligent feeding technique based on predicting shrimp growth in recirculating aquaculture system. Aquaculture Research, 53(12), 4401-4413.
  • Chen, J. C., Chen, T. L., Wang, H. L., Chang, P. C. (2022). Underwater abnormal classification system based on deep learning: A case study on aquaculture fish farm in Taiwan. Aquacultural Engineering, 99, 102290.
  • Claro, R., 1994. Características generales de la ictiofauna. p. 55-70. In R. Claro (ed.) Ecología de los peces marinos de Cuba. Instituto de Oceanología Academia de Ciencias de Cuba and Centro de Investigaciones de Quintana Roo.
  • Coro G., Large S., Magliozzi C., Pagano P., (2016b), Analysing and forecasting fisheries time series: purse seine in Indian Ocean as a case study, ICES Journal of Marine Science, 73, 2552-2571.
  • Coro G., Magliozzi C., Berghe E.V., Bailly N., Ellenbroek A., Pagano P., (2016a), Estimating absence locations of marine species from data of scientific surveys in OBIS, Ecological Modelling, 323, 61-76.
  • Coro G., Pagano P., Ellenbroek A., (2013a), Combining simulated expert knowledge with neural networks to produce ecological niche models for Latimeria chalumnae, Ecological Modelling, 268, 55-63.
  • Coro G., Pagano P., Ellenbroek A., (2018a), Detecting patterns of climate change in long-term forecasts of marine environmental parameters, International Journal of Digital Earth, 13, 1-19.
  • Coro G., Palma M., Ellenbroek A., Panichi G., Nair T., Pagano P., (2019), Reconstructing 3D virtual environments within a collaborative e-infrastructure, Concurrency and Computation: Practice and Experience, 31, e5028.
  • Coro G., Vilas L.G., Magliozzi C., Ellenbroek A., Scarponi P., Pagano P., (2018b), Forecasting the ongoing invasion of Lagocephalus sceleratus in the Mediterranean Sea, Ecological Modelling, 371, 37-49.
  • Coro, G. (2020). Open Science and Artificial Intelligence Supporting Blue Growth. Environmental Engineering and Management Journal, 19(10), 1719-1729.
  • Darapaneni, N., Sreekanth, S., Paduri, A. R., Roche, A. S., Murugappan, V., Singha, K. K., & Shenwai, A. V. (2022). AI based farm fish disease detection system to help micro and small fish farmers. In 2022 Interdisciplinary Research in Technology and Management (IRTM) (pp. 1-5). IEEE.
  • Dellermann, D., Ebel, P., Söllner, M., Leimeister, J.M. 2019. Hybrid intelligence. Bus Inf Syst Eng 61:637–643.
  • Desse J. and Desse-Berset¸ N 1994. Stratégies de pêche au 8ème millénaire les poissons de Cap AndreasKastros (Chypre). In: Le Brun A (eds) Fouilles récentes à Khirokitia, Editions Recherche Sur Civilisations.Vol. 3; pp. 335-360. Paris, France.
  • Erauskin-Extramiana, M., Chust, G., Arrizabalaga, H., Cheung, W.W., Santiago, J., Merino, G., Fernandes-Salvador, J.A., 2023. Implications for the global tuna fishing industry of climate change-driven alterations in productivity and body sizes. Glob. Planet. Chang. 222, 104055 https://doi.org/10.2139/ssrn.4059543.
  • FAO, (2020a), The Protected Areas Impact Maps Virtual Research Environment, i-Marine Gateway, On line at: https://imarine.d4science.org/web/protectedareaimpactmaps.
  • FAO. 2023. Using artificial intelligence to assess FAO’s knowledge base on the technology accelerator. Rome. https://doi.org/10.4060/cc6724en
  • FAO, 2024. Food And Agricultural Commodity Systems, https://www.undp.org/facs? (Erişim tarihi: 15.06.2024). Froese, R. and D. Pauly. Editors. 2024. FishBase. World Wide Web electronic publication. www.fishbase.org, version (06/2024).
  • Fromentin, J.M. and C. Ravier. 2005. The East Atlantic and Mediterranean bluefin tuna stock: looking for sustainability in a context of large uncertainties and strong political pressures. Bulletin of Marine Science Vol.76; pp. 353-362.
  • Gladjua, J., Kamalamb, B.K., Kanagara, A. 2022. Applications of data mining and machine learning framework in aquaculture and fisheries: A review, Smart Agricultural Technology, 2, 100061.
  • Goikoetxea, N., Goienetxea, I., Fernandes-Salvador, J. A., Goñi, N., Granado, I., Quincoces, I., Caballero, A. (2024). Machine-learning aiding sustainable Indian Ocean tuna purse seine fishery. Ecological Informatics, 81, 102577.
  • Gonçalves, D. N., Acosta, P. R., Ramos, A. P. M., Osco, L. P., Furuya, D. E. G., Furuya, M. T. G., Gonçalves, W. N. (2022). Using a convolutional neural network for fingerling counting: A multi-task learning approach. Aquaculture, 557, 738334.
  • Granado, I., Hernando, L., Galparsoro, I., Gabina, ˜ G., Groba, C., Prellezo, R., Fernandes, J.A., (2021). Towards a framework for fishing route optimization decision support systems: review of the state-of-the-art and challenges. J. Clean. Prod. 320, 128661 https://doi.org/10.1016/j.jclepro.2021.128661.
  • 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), 6037. https://doi.org/10.3390/su13116037. Huang, Y. P., & Khabusi, S. P. (2025). Artificial Intelligence of Things (AIoT) Advances in Aquaculture: A Review. Processes, 13(1), 73.
  • Janiesch, C., Zschech, P., Heinrich, K. 2021. Machine learning and deep learning. Electron Mark 31(3):685–695. Kaur, R., Kumar, R., Gupta, M. 2023. Deep neural network for food image classifcation and nutrient identifcation: A systematic review. Rev Endocr Metab Disord 1–21.
  • Lee, P. G., Lea, R. N., Dohmann, E., Prebilsky, W., Turk, P. E., Ying, H., Whitson, J. L. (2000). Denitrification in aquaculture systems: an example of a fuzzy logic control problem. Aquacultural Engineering, 23(1-3), 37-59.
  • Magliozzi C., Coro G., Grabowski R.C., Packman A.I., Krause S., (2019), A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning, Environmental Modelling and Software, 111, 311-323.
  • Magliozzi, C., Coro, G., Grabowski, R. C., Packman, A. I., & Krause, S. (2019). A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning. Environmental Modelling & Software, 111, 311-323. Coro ve diğ., 2015
  • Mather F.J, Mason J.M. and Jones A.C. (1995). Historical document: life history and fisheries of Atlantic bluefin tuna. Miami: NOAA Technical Memorandum 370, USA.
  • McCauley, D. J., Woods, P., Sullivan, B., Bergman, B., Jablonicky, C., Roan, A., ... & Worm, B. (2016). Ending hide and seek at sea. Science, 351(6278), 1148-1150.
  • Munoz-Benavent, P., Andreu-García, G., Valiente-González, J. M., Atienza-Vanacloig, V., Puig-Pons, V., & Espinosa, V. (2018). Automatic Bluefin Tuna sizing using a stereoscopic vision system. ICES Journal of Marine Science, 75(1), 390-401.
  • Muñoz-Benavent, P., Martínez-Peiró, J., Andreu-García, G., Puig-Pons, V., Espinosa, V., Pérez-Arjona, I., Ortega, A. (2022). Impact evaluation of deep learning on image segmentation for automatic bluefin tuna sizing. Aquacultural Engineering, 99, 102299.
  • 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 artifcial intelligence (CIA). Rev Aquac 13(4):2076–2091.
  • O’Donncha, F., Stockwell, C.L., Planellas, S.R., Micallef, G., Palmes, P., Webb, C., Grant, J. 2021. Data driven insight into fsh behaviour and their use for precision aquaculture. Front Anim Sci 2:695054.
  • Panudju, A.T., Rahardja, S., Nurilmala, M. 2023. Decision support system in fsheries industry: Current state and future agenda. Int J Adv Sci Eng Inf Technol 13(2).
  • Parker, R. W., & Tyedmers, P. H. (2015). Fuel consumption of global fishing fleets: current understanding and knowledge gaps. Fish and Fisheries, 16(4), 684-696.
  • Rojon, I., & Smith, T. W. P. (2014). On the attitudes and opportunities of fuel consumption monitoring and measurement within the shipping industry and the identification and validation of energy efficiency and performance interventions. https://discovery.ucl.ac.uk/id/eprint/1472842/
  • Suuronen, P., Chopin, F., Glass, C., Løkkeborg, S., Matsushita, Y., Queirolo, D., & Rihan, D. (2012). Low impact and fuel efficient fishing—Looking beyond the horizon. Fisheries research, 119, 135-146.
  • Telesca, J. E. (2020). Red gold: The managed extinction of the giant Bluefin Tuna. U of Minnesota Press. https://books.google.com.tr/books?hl=tr&lr=&id=VhbaDwAAQBAJ&oi=fnd&pg=PT5&ots=00vbQdD1lT&sig=cq9xNZGokX_QGwRq6t0pLcRyM9Q&redir_esc=y#v=onepage&q&f=false
  • Tilve, M., Rastogi, S., Gautam, R. S. (2024). Role of Artificial Intelligence in the Healthcare Sector in India: A Futuristic Study. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 1787-1791).
  • West, DM and Allen, JR (2018) How artificial intelligence is transforming the world. Center for Technology Innovation, The Brookings Institution
  • Uranga, J., Arrizabalaga, H., Boyra, G., Hernandez, M. C., Goni, N., Arregui, I, Santiago, J. (2017). Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels. PloS one, 12(2), e0171382.
  • Wang, C., Li, Z., Wang, T., Xu, X., Zhang, X., Li, D. 2021. Intelligent fsh farm—The future of aquaculture. Aquacult Int 1–31.
  • Wu, Y., Duan, Y., Wei, Y., An, D., Liu, J. (2022). Application of intelligent and unmanned equipment in aquaculture: A review. Computers and Electronics in Agriculture, 199, 107201.
  • Xu, G., Chen, Q., Yoshida, T., Teravama, K., Mizukami, Y., Li, Q., & Kitazawa, D. (2020). Detection of bluefin tuna by cascade classifier and deep learning for monitoring fish resources. In Global Oceans 2020: Singapore–US Gulf Coast (pp. 1-4). IEEE.
  • Yue, K., Shen, Y. (2022). An overview of disruptive technologies for aquaculture. Aquacult Fish 7(2):111–120. Zadeh, LA (1965) Fuzzy sets. Information and Control, 8:338-353 https://tuna-scope.com/en/ (Erişim tarihi: 15.04.2024).
  • Zhang, S., Yang, X., Wang, Y., Zhao, Z., Liu, J., Liu, Y., Zhou, C. 2020. Automatic fsh population counting by machine vision and a hybrid deep neural network model. Animals 10(2):364.
Toplam 53 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yaşam Bilimlerinde Bilgi İşleme, Balık Yetiştiriciliği
Bölüm Araştırma Makaleleri
Yazarlar

Oğulcan Kemal Sagun 0009-0002-9342-2968

Hülya Eminçe Saygı 0000-0002-3408-6709

Yayımlanma Tarihi 28 Mart 2025
Gönderilme Tarihi 28 Şubat 2025
Kabul Tarihi 20 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 1

Kaynak Göster

APA Sagun, O. K., & Eminçe Saygı, H. (2025). Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı ve Yetiştiriciliği’nin Rolü. Memba Su Bilimleri Dergisi, 11(1), 96-115.
AMA Sagun OK, Eminçe Saygı H. Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı ve Yetiştiriciliği’nin Rolü. Memba Su Bilimleri Dergisi. Mart 2025;11(1):96-115.
Chicago Sagun, Oğulcan Kemal, ve Hülya Eminçe Saygı. “Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı Ve Yetiştiriciliği’nin Rolü”. Memba Su Bilimleri Dergisi 11, sy. 1 (Mart 2025): 96-115.
EndNote Sagun OK, Eminçe Saygı H (01 Mart 2025) Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758) ’un Avcılığı ve Yetiştiriciliği’nin Rolü. Memba Su Bilimleri Dergisi 11 1 96–115.
IEEE O. K. Sagun ve H. Eminçe Saygı, “Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı ve Yetiştiriciliği’nin Rolü”, Memba Su Bilimleri Dergisi, c. 11, sy. 1, ss. 96–115, 2025.
ISNAD Sagun, Oğulcan Kemal - Eminçe Saygı, Hülya. “Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı Ve Yetiştiriciliği’nin Rolü”. Memba Su Bilimleri Dergisi 11/1 (Mart 2025), 96-115.
JAMA Sagun OK, Eminçe Saygı H. Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı ve Yetiştiriciliği’nin Rolü. Memba Su Bilimleri Dergisi. 2025;11:96–115.
MLA Sagun, Oğulcan Kemal ve Hülya Eminçe Saygı. “Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı Ve Yetiştiriciliği’nin Rolü”. Memba Su Bilimleri Dergisi, c. 11, sy. 1, 2025, ss. 96-115.
Vancouver Sagun OK, Eminçe Saygı H. Yapay Zeka Uygulamalarının Mavi Yüzgeçli Orkinos (Thunnus Thynnus (Linnaeus, 1758))’un Avcılığı ve Yetiştiriciliği’nin Rolü. Memba Su Bilimleri Dergisi. 2025;11(1):96-115.

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