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Integrating AIoT Technologies in Aquaculture: A Systematic Review

Author

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  • Fahmida Wazed Tina

    (Creative Innovation in Science and Technology Program, Faculty of Science and Technology, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat 80280, Thailand
    Mathematics Program (Statistics Major), Faculty of Science and Technology, Nakhon Si Thammarat Rajabhat University, Nakhon Si Thammarat 80280, Thailand)

  • Nasrin Afsarimanesh

    (School of Civil and Mechanical Engineering, Curtin University, Perth, WA 6102, Australia)

  • Anindya Nag

    (Faculty of Electrical and Computer Engineering, Technische Universität Dresden, 01062 Dresden, Germany
    Centre for Tactile Internet with Human-in-the-Loop (CeTI), Technische Universität Dresden, 01069 Dresden, Germany)

  • Md Eshrat E. Alahi

    (School of Engineering and Technology, Walailak University, 222 Thaiburi, Thasala District, Nakhon Si Thammarat 80160, Thailand
    Research Center for Intelligent Technology and Integration, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80160, Thailand)

Abstract

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.

Suggested Citation

  • Fahmida Wazed Tina & Nasrin Afsarimanesh & Anindya Nag & Md Eshrat E. Alahi, 2025. "Integrating AIoT Technologies in Aquaculture: A Systematic Review," Future Internet, MDPI, vol. 17(5), pages 1-35, April.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:5:p:199-:d:1646197
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    References listed on IDEAS

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    1. Fernando S. Paolo & David Kroodsma & Jennifer Raynor & Tim Hochberg & Pete Davis & Jesse Cleary & Luca Marsaglia & Sara Orofino & Christian Thomas & Patrick Halpin, 2024. "Satellite mapping reveals extensive industrial activity at sea," Nature, Nature, vol. 625(7993), pages 85-91, January.
    2. Yiyang Wang & Dehao Xu & Xianpeng Li & Wei Wang, 2024. "Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM," Mathematics, MDPI, vol. 12(5), pages 1-19, February.
    3. Fernando S. Paolo & David Kroodsma & Jennifer Raynor & Tim Hochberg & Pete Davis & Jesse Cleary & Luca Marsaglia & Sara Orofino & Christian Thomas & Patrick Halpin, 2024. "Author Correction: Satellite mapping reveals extensive industrial activity at sea," Nature, Nature, vol. 626(8000), pages 15-15, February.
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