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

Abstract

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.

Keywords

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

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

Abstract

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.

Keywords

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

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