Application of artificial intelligence in fish information identification: a scientometric perspective
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 recogni...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-04-01
|
| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1575523/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2296-7745 |