Analysis of the Use of Artificial Intelligence in Aquaculture through the Bibliometric Analysis Method

Öz

In this study, the development of artificial intelligence (AI) applications in aquaculture within the scientific literature was examined using the bibliometric analysis method. A total of 209 articles published between 2000 and 2024 were analyzed from the Web of Science database. The findings revealed that AI has been used in aquaculture for purposes such as population monitoring, disease diagnosis, water quality measurement, and productivity prediction. These technologies have been shown to optimize production processes while supporting environmental and economic sustainability. According to the analysis results, the number of publications has increased significantly, particularly after 2020. China is one of the leading countries in this field, with China Agricultural University being the institution with the highest number of publications. International collaborations are on the rise; however, broader interdisciplinary and global cooperation is still needed. In the literature, concepts such as “system,” “fish,” “model,” “aquaculture,” “prediction,” and “neural network” are prominent, indicating that AI applications are utilized in aquaculture at both biological and systemic levels in a versatile manner. The study emphasizes that AI applications in aquaculture not only enhance technical efficiency but also contribute to environmental and economic sustainability. It highlights that with increased data sharing and interdisciplinary collaboration in the future, smarter and more resilient production models are expected to be developed.

Anahtar Kelimeler

• artificial intelligence
• aquaculture
• machine learning
• decision support system
• deep learning

Su Ürünleri Yetiştiriciliğinde Yapay Zeka’nın kullanımının Bibliyometrik Analiz Yöntemi ile Analizi

Öz

Su ürünleri yetiştiriciliğinde yapay zekâ (YZ) uygulamalarının bilimsel literatürdeki gelişimi bibliyometrik analiz yöntemiyle incelendiği bu çalışmada; 2000-2024 yılları arasında Web of Science veri tabanından elde edilen 209 makale analiz edilmiştir. YZ'nin su ürünleri yetiştiriciliğinde popülasyon takibi, hastalık teşhisi, su kalitesi ölçümü ve verimlilik tahmini gibi alanlarda kullanıldığı; bu teknolojilerin üretim süreçlerini optimize ederek çevresel ve ekonomik sürdürülebilirliği desteklediği ortaya konmuştur. Analiz sonuçlarına göre, yayın sayılarının özellikle 2020 sonrası dönemde önemli bir artış gösterdiği tespit edilmiştir. Çin, bu alanda önde gelen ülkelerden biri olup China Agricultural University en yüksek yayın sayısına sahip kurumdur. Uluslararası işbirlikleri artmakta, ancak küresel çapta daha fazla disiplinler arası ve işbirliği gerekmektedir. Literatürde “system,” “fish,” “model,” “aquaculture,” “prediction” ve “neural network” gibi kavramlar ön plandadır ve yapay zekâ uygulamalarının hem biyolojik hem de sistemsel düzeyde çok yönlü kullanıldığını göstermektedir. Çalışma; su ürünleri yetiştiriciliğinde yapay zekâ uygulamalarının sadece teknik verimlilik sağlamakla kalmayıp aynı zamanda çevresel ve ekonomik sürdürülebilirliğe de katkıda bulunduğunu, gelecekte veri paylaşımı ve disiplinler arası işbirliklerinin artmasıyla daha akıllı ve dirençli üretim modellerinin geliştirileceğini vurgulamaktadır.

Anahtar Kelimeler

• yapay zeka
• su ürünleri yetiştiriciliği
• makine öğrenimi
• karar destek sistemi
• derin öğrenme

Kaynakça

1. 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.
2. Ahmed, M. S., Aurpa, T. T., & Azad, M. A. K. (2022). Fish disease detection using image based machine learning technique in aquaculture. Journal of King Saud University-Computer and Information Sciences, 34(8): 5170-5182.
3. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11(4): 959-975.
4. Aung, T., Razak, R. A., & Bin Md Nor, A. R. (2025). Artificial Intelligence Methods Used in Various Aquaculture Applications: A Systematic Literature Review. Journal of the World Aquaculture Society, 56, e13107. https://doi.org/10.1111/jwas.13107.
5. Bohara, K., Timilsina, A., Adhikari, K., Kafle, A., Basyal, S., Joshi, P., & Yadav, A. K. (2024). A mini review on 6PPD quinone: A new threat to aquaculture and fisheries. Environmental Pollution, 340, 122828.
6. Bozca, S., (2022). Rekrasyon Fırsat Dağılımı: Bibliyometrik Bir Araştırma. Yüksek Lisans Tezi. Akdeniz Üniversitesi, Sosyal Bilimler Enstitüsü, Türkiye.
7. Chen, F., Sun, M., Du, Y., Xu, J., Zhou, L., Qiu, T., & Sun, J. (2022b). Intelligent feeding technique based on predicting shrimp growth in recirculating aquaculture system. Aquaculture Research, 53(12), 4401-4413.Cobo, M. J., Martínez, M. A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-based systems, 80: 3-13.
8. Chen, J. C., Chen, T. L., Wang, H. L., & Chang, P. C. (2022a). Underwater abnormal classification system based on deep learning: A case study on aquaculture fish farm in Taiwan. Aquacultural Engineering, 99, 102290.

9. Cobo, M. J., Martínez, M. A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-based systems, 80, 3-13.
10. Cobo, M. J., Martínez, M. A., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-based systems, 80, 3-13.
11. Çakır, M., Oral, O., Ural, G. N., & Yılmaz, M. (2025). Mapping the research landscape of machine learning in aquaculture: A bibliometric analysis with forecasted output by 2030. Aquaculture, 743258.
12. de Castro Moura Duarte, A. L., Picanço Rodrigues, V., & Bonome Message Costa, L. (2024). The sustainability challenges of fresh food supply chains: an integrative framework. Environment, Development and Sustainability, 1-25.
13. Egghe, L., & Rousseau, R. (2002). Co-citation, bibliographic coupling and a characterization of lattice citation networks. Scientometrics, 55(3): 349-361.
14. Engle, C. R., van Senten, J., Tuckett, Q. M., & Hill, J. E. (2024). Potential economic impact on US aquaculture of injurious species listing of major aquaculture species. Journal of the World Aquaculture Society, 55(1): 89-109.
15. Gaviria-Marin, M., Merigó, J. M., & Baier-Fuentes, H. (2019). Knowledge management: A global examination based on bibliometric analysis. Technological Forecasting and Social Change, 140: 194-220.
16. Genç, G., Fırat, S., Yıldız HH., & Kaplan Z., (2020). Sağlık Politikası Konusunun Bilim Haritalama Teknikleri ile Analizi. Sağlık Politikası Konusunun Bilim Haritalama (Science Mapping) Teknikleri İie Analizi, İKSAD, 3. Bölüm, 74-115.
17. 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.
18. Gu, Y. (2004). Global knowledge management research: A bibliometric analysis. Scientometrics, 61: 171-190. Gupta, S., Wang, W., Hayek, S. S., Chan, L., Mathews, K. S., Melamed, M. L., & Modersitzki, F. (2021). Association between early treatment with tocilizumab and mortality among critically ill patients with COVID-19. JAMA Internal Medicine, 181(1): 41-51.
19. Handayani, T. P., Anggraeni, D. P., Ali, M., & Sari, M. P. (2025). Bibliometric Analysis Of Machine Learning Applications In Fisheries Management (2010-2023). Journal of Sustainability Science and Management, 20, 2263-2288.
20. Henriksen, D. (2016). The rise in co-authorship in the social sciences (1980–2013). Scientometrics, 107(2): 455-476.
21. Hu, J., & Zhang, Y. (2017). Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization. Scientometrics, 112(1): 91-109.
22. Islam, S., Hasan, A. M., Bhuiyan, M. A. R., & Bhat, G. (2024). Evaluation of environmental impacts of cotton polo shirt production in Bangladesh using life cycle assessment. Science of the Total Environment, 926: 172097.
23. Khandelwal, A., Weihs, L., Mottaghi, R., & Kembhavi, A. (2022). Simple but effective: Clip embeddings for embodied ai. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
24. Ma, F., Fan, Z., Nikolaeva, A., & Bao, H. (2025). Redefining Aquaculture Safety with Artificial Intelligence: Design Innovations, Trends, and Future Perspectives. Fishes, 10(3): 88. https://doi.org/10.3390/fishes10030088.
25. Ma, F., Fan, Z., Nikolaeva, A., & Bao, H. (2025). Redefining Aquaculture Safety with Artificial Intelligence: Design Innovations, Trends, and Future Perspectives. Fishes, 10(3), 88.
26. Mustapha, I., Van, N. T., Shahverdi, M., Qureshi, M. I., & Khan, N. (2021). Effectiveness of Digital Technology in Education During COVID-19 Pandemic. A Bibliometric Analysis. International Journal of Interactive Mobile Technologies, 15(8).
27. Noyons, E. C., Moed, H. F., & Van Raan, A. F. (1999). Integrating research performance analysis and science mapping. Scientometrics, 46(3): 591-604.
28. Okoli, C., & Schabram, K. (2012). A Guide to Conducting a Systematic Literature Review of Information Systems Research. SSRN Electronic Journal, 2012.
29. Ou, L., Lu, L., Qian, W., & Liu, B. (2025). Application of Artificial Intelligence in Fish Information Identification: A Scientometric Perspective. Frontiers in Marine Science, 12. https://doi.org/10.3389/fmars.2025.1575523.
30. Pritchard, A. (1969). Statistical Bibliography; An Interim Bibliography. Journal of Documentation, 25(4): 348–349.
31. Rashid, R., Sohrabi, C., Kerwan, A., Franchi, T., Mathew, G., Nicola, M., & Agha, R. A. (2024). The STROCSS 2024 guideline: strengthening the reporting of cohort, cross-sectional, and case–control studies in surgery. International Journal of Surgery, 110(6): 3151-3165.
32. Rastegari, H., Nadi, F., Lam, S. S., Ikhwanuddin, M., Kasan, N. A., Rahmat, R. F., & Mahari, W. A. W. (2023). Internet of Things in aquaculture: A review of the challenges and potential solutions based on current and future trends. Smart Agricultural Technology, 4: 100187.
33. Rejeb, A., Rejeb, K., & Keogh, J. G. (2025). The nexus of IoT and aquaculture: A bibliometric analysis. Applied Food Research, 100838.
34. Rowley, J., & Slack, F. (2004). Conducting a literature review. Management research news, 27(6), 31-39.
35. Şişik, L., (2022). Gastronomi alanındaki uluslararası araştırma eğilimleri: web of scıence (wos) veri tabanında yer alan 1995–2018 yıllarına ait makalelerin bibliyometrik analizi Doktora Tezi, İstanbul Üniversitesi.
36. Van Eck, N. J., & Waltman, L. (2022). Crossref as a source of open bibliographic metadata. MetaArXiv, 1-15. 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.
37. Yadav, P., Tudela, L. A. M., & Marco-Lajara, B. (2024). The role of AI in assessing and achieving the sustainable development goals (SDGs). In Issues of sustainability in AI and new-age thematic investing (pp. 1-17). IGI Global Scientific Publishing.
38. Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23: 100224.
39. Zhao, F., Shao, X., Wang, J., Chen, Y., Xi, D., Liu, Y., & Mizuno, K. (2025). Underwater Sea Cucumber Detection Using Consumer-Grade Amphibious UAV and Deep-Learning Based Computer Vision. In 2025 IEEE Underwater Technology (UT). IEEE. vd., 2025.