Review Maps AI Research in Fisheries Monitoring

Researchers F. Patangngari, M. Musbir, Najamuddin and colleagues published a bibliometric and synthesis review in the New Zealand Journal of Marine and Freshwater Research in 2026, mapping artificial intelligence research for capture fisheries monitoring and assessment. They analyzed 495 Scopus-indexed journal articles from 2015–2026 using a documented screening process assisted by a large language model and verified by humans. Publication output rose sharply, nearing an exponential increase after 2020.

Keyword co-occurrence analysis found that research themes align more closely with technical tasks—detection, classification and operational or vessel management—than with application domains. Acoustic AI did not form an independent theme and accounted for 8.1% of the corpus, compared with visual AI at 44.0% and vessel monitoring at 28.9%. The review separately synthesized three functional categories: visual AI for fish detection and measurement, vessel monitoring using AIS/VMS data, and acoustic support for surveys.

The researchers found that these areas are converging around a shared AI and machine-learning toolkit but remain largely siloed by technical application. China accounted for 85.9% of records, while international collaboration was limited to 23.64%. The review says research is moving beyond isolated proof-of-concept studies toward operational applications relevant to fisheries management. It identifies acoustic AI and integration across application areas as priorities for developing fisheries-oriented, evidence-based management.

Researchers F. Patangngari, M. Musbir, Najamuddin and colleagues published a bibliometric and synthesis review in the New Zealand Journal of Marine and Freshwater Research in 2026, mapping artificial intelligence research for capture fisheries monitoring and assessment. They analyzed 495 Scopus-indexed journal articles from 2015–2026 using a documented screening process assisted by a large language model and verified by humans. Publication output rose sharply, nearing an exponential increase after 2020.

Keyword co-occurrence analysis found that research themes align more closely with technical tasks—detection, classification and operational or vessel management—than with application domains. Acoustic AI did not form an independent theme and accounted for 8.1% of the corpus, compared with visual AI at 44.0% and vessel monitoring at 28.9%. The review separately synthesized three functional categories: visual AI for fish detection and measurement, vessel monitoring using AIS/VMS data, and acoustic support for surveys.

The researchers found that these areas are converging around a shared AI and machine-learning toolkit but remain largely siloed by technical application. China accounted for 85.9% of records, while international collaboration was limited to 23.64%. The review says research is moving beyond isolated proof-of-concept studies toward operational applications relevant to fisheries management. It identifies acoustic AI and integration across application areas as priorities for developing fisheries-oriented, evidence-based management.