Web of Science'da Yayımlanan Su Ürünleri Yetiştiriciliği ve Yapay Zeka Konulu Yayınların Kapsamlı Analizi

Year 2025, , 237 - 246, 30.05.2025

Hamdi Aydın

https://doi.org/10.35229/jaes.1644688

Abstract

Su ürünleri yetiştiriciliği, artan nüfus ve gıda talebi nedeniyle önem kazanmaktadır. Ancak sektördeki en büyük zorluklardan biri, yenilikçi teknolojilere duyulan ihtiyaçtır. Yapay zeka (YZ), çevresel süreçlerin yönetimi, hastalıkların erken tespiti, su kalitesinin izlenmesi ve beslenme stratejilerinin optimize edilmesi gibi konularda önemli çözümler sunmaktadır. Bu çalışma, 1998-2024 yılları arasında Web of Science veri tabanındaki 202 yayını analiz ederek yapay zekanın su ürünleri yetiştiriciliğindeki evrimini incelemektedir. Son yıllarda akademik üretkenlik hızla artmış, 2024’te 64 makaleye ulaşmıştır. En yaygın belge türleri "Makaleler" (124) ve "İncelemeler" (41) olup, araştırmalar Balıkçılık (41) ve Deniz Tatlı Su Biyolojisi (29) gibi çevre disiplinlerinin yanı sıra Mühendislik Elektrik Elektroniği (26) ve Bilgisayar Bilimi (25) gibi teknik alanlarda yoğunlaşmıştır. Aquaculture ve Computers and Electronics in Agriculture en önde gelen dergilerdir. Çin (52) ve ABD (28) en fazla katkı sağlayan ülkeler olup, Li Daoliang (7 yayın) en üretken yazarlardandır. Anahtar kelime analizi, "Su Ürünleri Yetiştiriciliği" (66), "Yapay Zeka" (61) ve "Makine Öğrenimi" (36) gibi merkezi temaları ortaya koyarken, "Akıllı Balık Çiftliği" ve "Sürdürülebilirlik" gibi kavramlar teknoloji odaklı çevreci çözümlere yönelimi göstermektedir. Atıf ağları, güçlü bağlantılar olsa da bazı parçalanmaların sürdüğünü ortaya koymaktadır. Bulgular, yapay zekanın sektördeki rolünü artırarak sürdürülebilirlik ve iş birliğini teşvik ettiğini göstermektedir.

Keywords

Akıllı balık çiftliği, su ürünleri yetiştiriciliği, yapay zeka, yayınlar, web of science.

A Comprehensive Analysis of Publications on Aquaculture and Artificial İntelligence, Published on Web of Science

Abstract

Aquaculture is gaining importance due to the increasing population and food demand. However, one of the biggest challenges in the sector is the need for innovative technologies. Artificial intelligence (AI) offers important solutions in environmental process management, early disease detection, water quality monitoring and optimizing feeding strategies. This study examines the evolution of AI in aquaculture by analyzing 202 publications in the Web of Science database between 1998 and 2024. Academic productivity has increased rapidly in recent years, reaching 64 articles in 2024. The most common document types are “Articles” (124) and “Reviews” (41), with research focused on environmental disciplines such as Fisheries (41) and Marine Freshwater Biology (29), as well as technical fields such as Engineering Electrical Electronics (26) and Computer Science (25). The leading journals are Aquaculture and Computers and Electronics in Agriculture. China (52) and the US (28) are the top contributors, with Li Daoliang (7 publications) being the most prolific author. Keyword analysis reveals central themes such as “Aquaculture” (66), “Artificial Intelligence” (61), and “Machine Learning” (36), while concepts such as “Smart Fish Farming” and “Sustainability” indicate a shift toward technology-driven green solutions. Citation networks reveal strong connections but some fragmentation. The findings suggest that AI is increasing its role in the industry, encouraging sustainability and collaboration.

Keywords

Aquaculture, artificial intelligence, publications, smart fish farming, web of science.

References

• Abdullah, A.F., Man, H.C., Mohammed, A., Abd Karim, M.M., Yunusa, S.U., & Jais, A.B.M. (2024). Charting the aquaculture internet of things impact: Key applications, challenges, and future trend. Aquaculture Reports, 39. 102358. DOI: 10.1016/j.aqrep.2024.102358
• Alprol, A.E., Mansour, A.T., Ibrahim, M.E., & Ashour, M. (2024). Artificial intelligence technologies revolutionizing wastewater treatment: current trends and future prospective. Water, 16(2), 314. DOI: 10.3390/w16020314
• Bi, Y., Xue, B., Briscoe, D., Vennell, R., & Zhang, M. (2023). A new artificial intelligent approach to buoy detection for mussel farming. Journal of the Royal Society of New Zealand, 53, 27-51. DOI: 10.1080/03036758.2022.2090966
• Boyd, C.E., McNevin, A.A., & Davis, R.P. (2022). The contribution of fisheries and aquaculture to the global protein supply. Food Security, 14, 805-827. DOI: 10.1007/s12571-021-01246-9
• Cai, Y., Yao, Z., Jiang, H., Qin, W., Xiao, J., Huang, X., Pan, J., & Feng, H. (2024). Rapid detection of fish with SVC symptoms based on machine vision combined with a NAM-YOLO v7 hybrid model. Aquaculture, 582, 740558. DOI: 10.1016/j.aquaculture.2024.740558
• Carbajal, J.J., & Sánchez, L.P. (2008). Classification Based on Fuzzy Inference Systems for Artificial Habitat Quality in Shrimp Farming. In: 2008 Seventh Mexican International Conference on Artificial Intelligence, 388-392, DOI: 10.1109/MICAI.2008.70
• Chen, J., Zhang, D., Yang, S., & Nanehkaran, Y.A. (2020). Intelligent monitoring method of water quality based on image processing and RVFL- GMDH model. IET Image Processing, 14; 4646– 4656. DOI:10.1049/iet-ipr.2020.0254
• Chen, J.C., Chang, N., & Shieh, W. (2003). Assessing wastewater reclamation potential by neural network model. Engineering Applications of Artificial Intelligence, 16, 149-157. DOI: 10.1016/S0952-1976(03)00056-3
• Chiu, M.C., Yan, W.M., Bhat, S.A., & Huang, N.F. (2022a). Development of smart aquaculture farm management system using IoT and AI-based surrogate models. Journal of Agriculture and Food Research, 9, 100357. DOI: 10.1016/j.jafr.2022.100357
• Chiu, C.C., Liao, T.L., Chen, C.H., & Kao, S.E. (2022b). AIoT Precision Feeding Management System. Electronics, 11, 3358. DOI: 10.3390/electronics11203358
• Dupont, C., Cousin, P., & Dupont, S. (2018). Iot for aquaculture 4.0 smart and easy-to-deploy real- time water monitoring with iot. In: 2018 Global Internet of Things Summit (GIoTS), pp. 1-5. IEEE
• FAO. (2024). The State of World Fisheries and Aquaculture 2024-Blue Transformation in action. Rome. DOI: 10.4060/cd0683en
• Fernandes, A.F.A., Turra, E.M., De Alvarenga, É.R., Passafaro, T.L., Lopes, F.B., Alves, G.F.O., Singh, V., & Rosa, G.J.M. (2020). Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia. Computers and Electronics in Agriculture, DOI: 10.10 16/j.compag.2020.1 05274
• 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. DOI: 10.1016/j.atech.2022.100061
• Guo, H., Tao, X., & Li, X. (2023). Water quality image classification for aquaculture using deep transfer learning. Neural Network World, 1, 1-18. DOI: 10.14311/NNW.2023.33.001
• Hernández, J.J.C., Fernández, L.P.S., & Ibarra, M.A.M. (2010). Assessment of the artificial habitat in shrimp aquaculture using environmental pattern classification. Lecture Notes in Computer Science (including subseries in Artificial Intelligence and Lecture Notes in Bioinformatics) 6134 (LNCS), 113-121.
• Hernandez, J.J., Fernandez, L.P.S., & Pogrebnyak, O. (2011). Assessment and prediction of water quality in shrimp culture using signal processing techniques. Aquaculture International, 19 (6) (2011) 1083-1104. DOI: 10.1007/s10499-011- 9426-z.
• Hu, Z., Li R., Xia, X., Yu, C., Fan, X., & Zhao, Y. (2020). A method overview in smart aquaculture. Environmental Monitoring and Assessment, 192, 1-25. DOI: 10.1007/s10661-020-08409-9
• Igwegbe, C.A., Obi, C.C., Ohale, P.E., Ahmadi, S., Onukwuli, O.D., Nwabanne, J.T., & Białowiec A. (2023). Modelling and optimisation of electrocoagulation/flocculation recovery of effluent from land-based aquaculture by artificial intelligence (AI) approaches. Environmental Science and Pollution Research International, 30(27),70897-70917. DOI: 10.1007/s11356-023- 27387-2
• Jin, Y.C., Liu, J.Z, Xu, Z.J, Yuan, S., Q, Li, PP., & Wang, J.Z. (2021). Development status and trend of agricultural robot technology. International Journal of Agricultural and Biological Engineering, 14(4), 1-12. DOI: 10.25165/j.ijabe.20211404.6821
• Karimanzira, D., & Rauschenbach, T. (2021). An intelligent management system for aquaponics. Automatisier ungstechnik, 69, 345-350. DOI: 10.1515/AUTO-2020-0036
• Lea, R., Dohmann, E., Prebilsky, W., Lee, P., Turk, P., & Ying, H. (1998). A fuzzy logic application to aquaculture environment control. Annual Conference of the North American Fuzzy İnformation Processing Society - NAFİPS, 29-33.
• Lee, P.G. (2000). Process control and artificial intelligence software for aqua culture. Aquacultural Engineering, 23(1), 13-36. DOI: 10.1016/S0144- 8609(00)00044-3
• Lim, H.R., Khoo, K.S., Chia, W.Y., Chew, K.W., Ho, S.H., & Show, P.L. (2022). Smart microalgae farming with internet-of-things for sustainable agriculture. Biotechnology Advances. 57, 107931. DOI: 10.1016/j.biotechadv.2022.107931
• Lu, Y., Chen, D., Olaniyi, E., & Huang, Y. (2022). Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review. Computers and Electronics in Agriculture, 200, Article 107208. DOI: 10.1016/j. compag.2022.107208
• 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, 2076-2091. DOI: 10.1111/raq.12559
• Nguyen, N.H., Vu, N.T., Patil, S.S. & Sandhu, K.S. (2022). Multivariate genomic prediction for commercial traits of economic importance in Banana shrimp Fenneropenaeus merguiensis. Aquaculture, 555, 738229. DOI: 10.1016/j.aquaculture.2022.738229
• Ubina, N.A., Lan, H.Y., Cheng, S.C., Chang, C.C., Lin, S.Y., Zhang, K.X., Lu, H.Y., Cheng, C.Y., & Hsieh, Y.Z. (2023). Digital twin-based intelligent fish farming with artificial intelligence internet of things(AIoT). Smart Agricultural Technology, 5, 100285. DOI: 10.1016/j.atech.2023.10028
• Uz, S.S., Ames, T.J., Memarsadeghi N.,, McDonnell, S.M., Blough, N.V., Mehta, A.V., & McKay, J.R. (2020). Supporting aquaculture in the chesapeake bay using artificial intelligence to detect poor water quality with remote sensing. In paper presented at the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), USA. DOI: 10.1109/IGARSS39084.2020.9323465
• Vo, T.T.E., Ko, H., Huh, J.H., & Kim, Y. (2021). Overview of smart aquaculture system: focusing on applications of machine learning and computer vision. Electronics 10, 2882. DOI: 10.3390/electronics10222882
• Wagle, N., Acharya, T.D. & Lee, D.H. (2020). Comprehensive review on application of machine learning algorithms for water quality parameter estimation using remote sensing data, Sensors & Materials, 32(11), 3879-3892. DOI: 10.18494/SAM.2020.2953
• Wang, C., Li, Z., Wang, T., Xu, X., Zhang, X., & Li, D. (2021a) Intelligent fish farm-the future of aquaculture. Aquaculture International, 29, 2681- 2711. DOI: 10.1007/s10499-021-00773-8
• Wang, T., Xu, X., Wang, C., Li, Z., & Li, D. (2021b). From smart farming towards unmanned farms: A new mode of agricultural production. Agriculture 11(4), 145. DOI: 10.3390/agriculture11020145
• Wu, Y., Duan, Y., Wei, Y., An, D., & Liu. J. (2022). Application of Intelligentand Unmanned Equipment in Aquaculture: A Review. Computers and Electronics in Agriculture, 199, 107201. DOI: 10.1016/j.compag.2022.107201
• Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S., & Zhou, C. (2021a). Deep learning for smart fish farming: applications, opportunities and challenges. Reviews in Aquaculture, 13(1), 66-90. DOI: 10.1111/raq.12464
• Yang, L., Liu, Y., Yu, H., Fang, X., Song, L., Li, D., & Chen, Y. (2021b). Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: A review. Archives of Computational Methods in Engineering, 28, 2785-2816. DOI: 10.1007/s11831-020-09486-2
• Zenger, K.R., Khatkar, M.S., Jones, D.B., Khalilisamani, N., Jerry, D.R., & Raadsma, H.W. (2019). Genomic selection in aquaculture: application, limitations and opportunities with special reference to marine shrimp and pearl oysters. Frontiers in Genetics. 9, 693. DOI: 10.3389/fgene.2018.00693
• Zha, J. (2020). Artificial Intelligence in Agriculture, Journal of Physics: Conference Series, 2020. DOI: 10.1088/1742-6596/1693/1/012058
• Zhao, S., Zhang, S., Liu, J., Wang, H., Zhu, J., Li, D., & Zhao, R. (2021). Application of machine learning in intelligent fish aquaculture: A review. Aquaculture, 540 (1), 736724. DOI: 10.1016/j.aquaculture.2021.736724.