Integrating AIoT Technologies in Aquaculture: A Systematic Review
The increasing global demand for seafood underscores the necessity for sustainable aquaculture practices. However, several challenges, including rising operational costs, variable environmental conditions, and the threat of disease outbreaks, impede progress in this field. This review explores the t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/5/199 |
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| author | Fahmida Wazed Tina Nasrin Afsarimanesh Anindya Nag Md Eshrat E. Alahi |
| author_facet | Fahmida Wazed Tina Nasrin Afsarimanesh Anindya Nag Md Eshrat E. Alahi |
| author_sort | Fahmida Wazed Tina |
| collection | DOAJ |
| description | The increasing global demand for seafood underscores the necessity for sustainable aquaculture practices. However, several challenges, including rising operational costs, variable environmental conditions, and the threat of disease outbreaks, impede progress in this field. This review explores the transformative role of the Artificial Intelligence of Things (AIoT) in mitigating these challenges. We analyse current research on AIoT applications in aquaculture, with a strong emphasis on the use of IoT sensors for real-time data collection and AI algorithms for effective data analysis. Our focus areas include monitoring water quality, implementing smart feeding strategies, detecting diseases, analysing fish behaviour, and employing automated counting techniques. Nevertheless, several research gaps remain, particularly regarding the integration of AI in broodstock management, the development of multimodal AI systems, and challenges regarding model generalization. Future advancements in AIoT should prioritise real-time adaptability, cost-effectiveness, and sustainability while emphasizing the importance of multimodal systems, advanced biosensing capabilities, and digital twin technologies. In conclusion, while AIoT presents substantial opportunities for enhancing aquaculture practices, successful implementation will depend on overcoming challenges related to scalability, cost, and technical expertise, improving models’ adaptability, and ensuring environmental sustainability. |
| format | Article |
| id | doaj-art-49dda059e767427ebda032d76ea41be0 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-49dda059e767427ebda032d76ea41be02025-08-20T03:47:57ZengMDPI AGFuture Internet1999-59032025-04-0117519910.3390/fi17050199Integrating AIoT Technologies in Aquaculture: A Systematic ReviewFahmida Wazed Tina0Nasrin Afsarimanesh1Anindya Nag2Md Eshrat E. Alahi3Creative Innovation in Science and Technology Program, Faculty of Science and Technology, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat 80280, ThailandSchool of Civil and Mechanical Engineering, Curtin University, Perth, WA 6102, AustraliaFaculty of Electrical and Computer Engineering, Technische Universität Dresden, 01062 Dresden, GermanySchool of Engineering and Technology, Walailak University, 222 Thaiburi, Thasala District, Nakhon Si Thammarat 80160, ThailandThe increasing global demand for seafood underscores the necessity for sustainable aquaculture practices. However, several challenges, including rising operational costs, variable environmental conditions, and the threat of disease outbreaks, impede progress in this field. This review explores the transformative role of the Artificial Intelligence of Things (AIoT) in mitigating these challenges. We analyse current research on AIoT applications in aquaculture, with a strong emphasis on the use of IoT sensors for real-time data collection and AI algorithms for effective data analysis. Our focus areas include monitoring water quality, implementing smart feeding strategies, detecting diseases, analysing fish behaviour, and employing automated counting techniques. Nevertheless, several research gaps remain, particularly regarding the integration of AI in broodstock management, the development of multimodal AI systems, and challenges regarding model generalization. Future advancements in AIoT should prioritise real-time adaptability, cost-effectiveness, and sustainability while emphasizing the importance of multimodal systems, advanced biosensing capabilities, and digital twin technologies. In conclusion, while AIoT presents substantial opportunities for enhancing aquaculture practices, successful implementation will depend on overcoming challenges related to scalability, cost, and technical expertise, improving models’ adaptability, and ensuring environmental sustainability.https://www.mdpi.com/1999-5903/17/5/199Artificial Intelligence of Things (AIoT)aquaculturesustainable practices |
| spellingShingle | Fahmida Wazed Tina Nasrin Afsarimanesh Anindya Nag Md Eshrat E. Alahi Integrating AIoT Technologies in Aquaculture: A Systematic Review Future Internet Artificial Intelligence of Things (AIoT) aquaculture sustainable practices |
| title | Integrating AIoT Technologies in Aquaculture: A Systematic Review |
| title_full | Integrating AIoT Technologies in Aquaculture: A Systematic Review |
| title_fullStr | Integrating AIoT Technologies in Aquaculture: A Systematic Review |
| title_full_unstemmed | Integrating AIoT Technologies in Aquaculture: A Systematic Review |
| title_short | Integrating AIoT Technologies in Aquaculture: A Systematic Review |
| title_sort | integrating aiot technologies in aquaculture a systematic review |
| topic | Artificial Intelligence of Things (AIoT) aquaculture sustainable practices |
| url | https://www.mdpi.com/1999-5903/17/5/199 |
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