Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context
Aquaculture is an essential productive activity in food security, economy, and the sustainability of water resources globally. The study analyzes the application of artificial intelligence techniques in aquaculture on the American continent through a systematic review of 31 articles published betwee...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2026
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| _version_ | 1881508986918797312 |
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| author | Levano Rodriguez, Danny Pinedo, Lloy Vizalote, Gorky Navas-Vásquez, Manuel Enrique López Gonzales, Javier Linkolk |
| author_browse | Levano Rodriguez, Danny López Gonzales, Javier Linkolk Navas-Vásquez, Manuel Enrique Pinedo, Lloy Vizalote, Gorky |
| author_facet | Levano Rodriguez, Danny Pinedo, Lloy Vizalote, Gorky Navas-Vásquez, Manuel Enrique López Gonzales, Javier Linkolk |
| author_sort | Levano Rodriguez, Danny |
| collection | Producción científica |
| description | Aquaculture is an essential productive activity in food security, economy, and the sustainability of water resources globally. The study analyzes the application of artificial intelligence techniques in aquaculture on the American continent through a systematic review of 31 articles published between 2020 and 2024 in the Scopus and SciELO databases. Five key areas of application were identified: monitoring and control, organism identification and counting, biomass and mortality rate prediction, behavioral analysis, and production optimization. The most commonly used techniques include machine learning, deep learning, artificial vision, and genetic algorithms, with models such as Convolutional Neural Networks, Random Forest, and YOLO standing out, demonstrating high accuracy in aquaculture processes. However, research has focused mainly on fish, while other organisms, such as shellfish and shrimp, have received less attention. In addition, adopting these technologies faces challenges related to infrastructure, data availability, and staff training. It is concluded that the integration of AI in aquaculture has a high potential to improve the efficiency and sustainability of the sector. However, it is necessary to expand the study to other species and strengthen technological accessibility for small and medium-sized producers. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/388 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/3882026-10-30T16:14:36ZApplication of artificial intelligence techniques in the aquaculture sector: Systematic review of the American contextLevano Rodriguez, DannyPinedo, LloyVizalote, GorkyNavas-Vásquez, Manuel EnriqueLópez Gonzales, Javier Linkolkaquaculture productionaquaculturemachine learningdeep learningcomputer visionAquaculture is an essential productive activity in food security, economy, and the sustainability of water resources globally. The study analyzes the application of artificial intelligence techniques in aquaculture on the American continent through a systematic review of 31 articles published between 2020 and 2024 in the Scopus and SciELO databases. Five key areas of application were identified: monitoring and control, organism identification and counting, biomass and mortality rate prediction, behavioral analysis, and production optimization. The most commonly used techniques include machine learning, deep learning, artificial vision, and genetic algorithms, with models such as Convolutional Neural Networks, Random Forest, and YOLO standing out, demonstrating high accuracy in aquaculture processes. However, research has focused mainly on fish, while other organisms, such as shellfish and shrimp, have received less attention. In addition, adopting these technologies faces challenges related to infrastructure, data availability, and staff training. It is concluded that the integration of AI in aquaculture has a high potential to improve the efficiency and sustainability of the sector. However, it is necessary to expand the study to other species and strengthen technological accessibility for small and medium-sized producers.Articlehttps://doaj.org/article/f891caec686046408d198a174afbb97c10.4025/actascianimsci.v48.i1.75518https://hdl.handle.net/20.500.15037/388W7164174805enActa Scientiarum Animal Sciences1806-26361807-86722026-06-012026-06-13T08:12:27Z2026-06-13T08:12:27Z |
| spellingShingle | aquaculture production aquaculture machine learning deep learning computer vision Levano Rodriguez, Danny Pinedo, Lloy Vizalote, Gorky Navas-Vásquez, Manuel Enrique López Gonzales, Javier Linkolk Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context |
| title | Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context |
| title_full | Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context |
| title_fullStr | Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context |
| title_full_unstemmed | Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context |
| title_short | Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context |
| title_sort | application of artificial intelligence techniques in the aquaculture sector systematic review of the american context |
| topic | aquaculture production aquaculture machine learning deep learning computer vision |
| topic_facet | aquaculture production aquaculture machine learning deep learning computer vision |
| url | https://doaj.org/article/f891caec686046408d198a174afbb97c https://hdl.handle.net/20.500.15037/388 |
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