Application of artificial intelligence in fish information identification: a scientometric perspective

IF 2.8 2区生物学 Q1 MARINE & FRESHWATER BIOLOGY

Frontiers in Marine Science Pub Date : 2025-04-15 DOI:10.3389/fmars.2025.1575523

Liguo Ou, Linlin Lu, Weiguo Qian, Bilin Liu

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引用次数: 0

Abstract

In the context of the growing demand for the sustainable development and conservation of fish stocks, artificial intelligence (AI) technologies are essential for supporting scientific fish stock management. Artificial intelligence technology provides an effective solution for the intelligent recognition of fish information. This study used bibliometric analysis to review a sample of 719 scientific articles from the WoSCC (Web of Science Core Collection) database from 2014-2024. The results revealed a significant increase in the number of publications from 2014-2024, with publications mainly from China, the USA (the United States) and other developed countries. The top three impactful journals are Ecological Informatics, Computers and Electronics in Agriculture and the ICES Journal of Marine Science. The most frequent keyword co-occurrence analysis was deep learning, and the best keyword clustering effect was computer vision. The findings indicate that this bibliometric evaluation provides a holistic visualization of the research frontier of AI in fish information identification, and our findings underscore the growing global importance of AI in fish information identification research and highlight publication trends, hotspots, and future research directions in this area. In conclusion, our findings provide valuable insights into the emerging frontiers of AI-based fish information identification.

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人工智能在鱼类信息识别中的应用：科学计量学视角

在对鱼类资源可持续发展和保护的需求日益增长的背景下，人工智能技术对于支持科学的鱼类资源管理至关重要。人工智能技术为鱼类信息的智能识别提供了有效的解决方案。本研究采用文献计量学分析方法对2014-2024年wscc （Web of Science Core Collection）数据库中的719篇科学论文进行了分析。结果显示，2014-2024年期间，论文发表数量显著增加，主要来自中国、美国和其他发达国家。影响力最大的三种期刊是《生态信息学》、《农业计算机与电子学》和《ICES海洋科学期刊》。关键词共现分析频率最高的是深度学习，关键词聚类效果最好的是计算机视觉。研究结果表明，本文的文献计量评估提供了人工智能在鱼类信息识别领域的研究前沿的整体可视化，强调了人工智能在鱼类信息识别研究中的全球重要性，并突出了该领域的出版趋势、热点和未来的研究方向。总之，我们的研究结果为基于人工智能的鱼类信息识别的新兴领域提供了有价值的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Marine Science Agricultural and Biological Sciences-Aquatic Science

CiteScore

5.10

自引率

16.20%

发文量

2443

审稿时长

14 weeks

期刊介绍： Frontiers in Marine Science publishes rigorously peer-reviewed research that advances our understanding of all aspects of the environment, biology, ecosystem functioning and human interactions with the oceans. Field Chief Editor Carlos M. Duarte at King Abdullah University of Science and Technology Thuwal is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, policy makers and the public worldwide. With the human population predicted to reach 9 billion people by 2050, it is clear that traditional land resources will not suffice to meet the demand for food or energy, required to support high-quality livelihoods. As a result, the oceans are emerging as a source of untapped assets, with new innovative industries, such as aquaculture, marine biotechnology, marine energy and deep-sea mining growing rapidly under a new era characterized by rapid growth of a blue, ocean-based economy. The sustainability of the blue economy is closely dependent on our knowledge about how to mitigate the impacts of the multiple pressures on the ocean ecosystem associated with the increased scale and diversification of industry operations in the ocean and global human pressures on the environment. Therefore, Frontiers in Marine Science particularly welcomes the communication of research outcomes addressing ocean-based solutions for the emerging challenges, including improved forecasting and observational capacities, understanding biodiversity and ecosystem problems, locally and globally, effective management strategies to maintain ocean health, and an improved capacity to sustainably derive resources from the oceans.