Key Takeaways
- Predictive AI optimized feed conversion ratios by 28% in shrimp ponds Vietnam 2023 using growth forecasts
- AI computer vision systems achieved 98.7% accuracy in identifying 52 fish species in the Pacific Ocean using drone footage from 2022 surveys
- Machine learning forecasted 92.4% accuracy Atlantic cod migration patterns using historical data from 2018-2023
- AI in traceability cut supply chain costs 29% global aquaculture 2024 per IBM
- AI reduced discards reporting errors 49% compliance fleets EU 2023
Record numbers of anglers and improved catch reporting are driving smarter decisions across the fishing industry.
Related reading
01 · Category
Aquaculture Management28 stats
Aquaculture Management Interpretation
02 · Category
Fish Detection And Identification29 stats
Fish Detection And Identification Interpretation
03 · Category
Predictive Analytics And Forecasting26 stats
Predictive Analytics And Forecasting Interpretation
More related reading
04 · Category
Supply Chain And Market Optimization29 stats
Supply Chain And Market Optimization Interpretation
05 · Category
Sustainability And Regulation Compliance26 stats
Sustainability And Regulation Compliance Interpretation
Where AI delivers the biggest performance gains in aquaculture
Across common aquaculture use-cases, AI is shown boosting accuracy, cutting mortality, and improving production—highlighting its measurable impact from detection to operations.
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Daniel Varga. (2026, February 13). AI In The Fishing Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-fishing-industry-statistics
Daniel Varga. "AI In The Fishing Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-fishing-industry-statistics.
Daniel Varga. 2026. "AI In The Fishing Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-fishing-industry-statistics.
Sources & references
100 datasets cited across this report · attribution is report-level

