AI-driven aquaculture: A review of technological innovations and their sustainable impacts
The integration of artificial intelligence (AI) in aquaculture has been identified as a transformative force, enhancing various operational aspects from water quality management to genetic optimization. This review provides a comprehensive synthesis of recent advancements in AI applications within t...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-09-01
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| Series: | Artificial Intelligence in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721725000182 |
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| author | Hang Yang Qi Feng Shibin Xia Zhenbin Wu Yi Zhang |
| author_facet | Hang Yang Qi Feng Shibin Xia Zhenbin Wu Yi Zhang |
| author_sort | Hang Yang |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) in aquaculture has been identified as a transformative force, enhancing various operational aspects from water quality management to genetic optimization. This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector, underscoring the significant enhancements in production efficiency and environmental sustainability. Key AI-driven improvements, such as predictive analytics for disease management and optimized feeding protocols, are highlighted, demonstrating their contributions to reducing waste and improving biomass outputs. However, challenges remain in terms of data quality, system integration, and the socio-economic impacts of technological adoption across diverse aquacultural environments. This review also addresses the gaps in current research, particularly the lack of robust, scalable AI models and frameworks that can be universally applied. Future directions are discussed, emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture. This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture. |
| format | Article |
| id | doaj-art-4e0b0186e2fd4e2c880b736ae544ce4c |
| institution | OA Journals |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-4e0b0186e2fd4e2c880b736ae544ce4c2025-08-20T01:53:08ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-09-0115350852510.1016/j.aiia.2025.01.012AI-driven aquaculture: A review of technological innovations and their sustainable impactsHang Yang0Qi Feng1Shibin Xia2Zhenbin Wu3Yi Zhang4Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, PR China; Hubei Key Laboratory of Mineral Resources Processing and Environment, Wuhan University of Technology, Wuhan 430070, ChinaKey Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, PR China; University of Chinese Academy of Sciences, Beijing 100049, ChinaHubei Key Laboratory of Mineral Resources Processing and Environment, Wuhan University of Technology, Wuhan 430070, China; Corresponding author.Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, PR ChinaKey Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, PR China; University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author at: Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, PR China.The integration of artificial intelligence (AI) in aquaculture has been identified as a transformative force, enhancing various operational aspects from water quality management to genetic optimization. This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector, underscoring the significant enhancements in production efficiency and environmental sustainability. Key AI-driven improvements, such as predictive analytics for disease management and optimized feeding protocols, are highlighted, demonstrating their contributions to reducing waste and improving biomass outputs. However, challenges remain in terms of data quality, system integration, and the socio-economic impacts of technological adoption across diverse aquacultural environments. This review also addresses the gaps in current research, particularly the lack of robust, scalable AI models and frameworks that can be universally applied. Future directions are discussed, emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture. This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture.http://www.sciencedirect.com/science/article/pii/S2589721725000182Aquaculture AI integrationPredictive analyticsSustainable aquacultureAI disease managementAI feeding optimization |
| spellingShingle | Hang Yang Qi Feng Shibin Xia Zhenbin Wu Yi Zhang AI-driven aquaculture: A review of technological innovations and their sustainable impacts Artificial Intelligence in Agriculture Aquaculture AI integration Predictive analytics Sustainable aquaculture AI disease management AI feeding optimization |
| title | AI-driven aquaculture: A review of technological innovations and their sustainable impacts |
| title_full | AI-driven aquaculture: A review of technological innovations and their sustainable impacts |
| title_fullStr | AI-driven aquaculture: A review of technological innovations and their sustainable impacts |
| title_full_unstemmed | AI-driven aquaculture: A review of technological innovations and their sustainable impacts |
| title_short | AI-driven aquaculture: A review of technological innovations and their sustainable impacts |
| title_sort | ai driven aquaculture a review of technological innovations and their sustainable impacts |
| topic | Aquaculture AI integration Predictive analytics Sustainable aquaculture AI disease management AI feeding optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2589721725000182 |
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