International Journal of Aquaculture, 2026, Vol.16, No.1, 32-45 http://www.aquapublisher.com/index.php/ija 43 Despite these challenges, intelligent fisheries still hold significant potential to support sustainable development when technological innovation is combined with inclusiveness, people-centered approaches, and ecological protection. Existing applications of IoT monitoring, AI analysis, and blockchain traceability have already improved efficiency, reduced waste, and increased transparency, while helping small-scale fishers access data and markets more effectively. AIoT and marine intelligent technologies can support adaptive management and ecological restoration by providing real-time information on resources and fishing intensity, and Industry 4.0 approaches may further reduce environmental impacts. Nevertheless, intelligent technologies are not a universal solution. Their long-term value depends on lowering application costs, improving digital skills, and establishing fair data-sharing mechanisms that reflect the realities of small-scale fisheries. Ultimately, intelligent technology is a tool for transformation, and its effectiveness rests on how well it is integrated into institutional reforms and evolving interest relationships within the fishery sector. Acknowledgments The authors extend sincere thanks to two anonymous peer reviewers for their feedback on the manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Adnan J., Ishak M., Hashim F., Saad M., and Rahman M., 2025, IoT-based coastal fisheries monitoring system, Advances in Agricultural and Food Research Journal, 10: a0000499. https://doi.org/10.36877/aafrj.a0000499 Afrifa‐Yamoah E., 2025, Digital transformation in recreational fisheries monitoring: A framework for data integration, analysis, and management applications, Fisheries Management and Ecology, 10: 70023. https://doi.org/10.1111/fme.70023 Agmata A., and Guðmundsson S., 2025, Convolutional-LSTM approach for temporal catch hotspots (CATCH): An AI-driven model for spatiotemporal forecasting of fisheries catch probability densities, Biology Methods & Protocols, 10: bpaf045. https://doi.org/10.1093/biomethods/bpaf045 Al-Abri S., Keshvari S., Al-Rashdi K., Al-Hmouz R., and Bourdoucen H., 2025, Computer vision-based approaches for fish monitoring systems: A comprehensive study, Artificial Intelligence Review, 58: 11180-3. https://doi.org/10.1007/s10462-025-11180-3 Alghamdi M., and Haraz Y., 2025, Smart biofloc systems: Leveraging artificial intelligence (AI) and internet of things (IoT) for sustainable aquaculture practices, Processes, 13(7): 2204. https://doi.org/10.3390/pr13072204 Baena-Navarro R., Carriazo-Regino Y., Torres-Hoyos F., and Pinedo-López J., 2025, Intelligent prediction and continuous monitoring of water quality in aquaculture: Integration of machine learning and internet of things for sustainable management, Water, 17(1): 82. https://doi.org/10.3390/w17010082 Baker L., Knott N., Gorkin R., Aubin S., Brown C., and Peters K., 2025, Fishing for data: AI approaches to advance recreational fisheries monitoring, New Zealand Journal of Marine and Freshwater Research, 59(8): 848-865. https://doi.org/10.1080/00288330.2024.2438227 Barreiro M., Abad E., Antelo L., Fernández J., Pereira C., Ovalle J., Martín R., and Valeiras J., 2025, Development of smart electronic observation onboard technologies for more sustainable fisheries management, Frontiers in Marine Science, 10: 1545718. https://doi.org/10.3389/fmars.2025.1545718 Briones S., Briones S., Isorena J., Amata M., and Sabaria J., 2025, The design and development of CATCHFISH system: A collaborative ICT-based tool for an optimize fishing repository and catch efficiency, Journal of Information Systems Engineering and Management, 10(32): 5431. https://doi.org/10.52783/jisem.v10i32s.5431 Chandravanshi S., Sudan P., Bihari K., Venkat C., Sandeep J., Yadav B., Revathi A., and P P., 2025, Digital innovations in fisheries and aquaculture: A systematic review of technologies, adoption, and socio-economic impacts, International Journal of Advanced Biochemistry Research, 9(9): 5642. https://doi.org/10.33545/26174693.2025.v9.i9e.5642 Chen L.Q., and Huang W.Z., 2025, Sustainable fisheries management: Balancing resource use and conservation, International Journal of Aquaculture, 15(6): 287-297. Huang W.Z., and Han Y.P., 2025, Habitat degradation and restoration in aquatic ecosystems: Implications for fish populations, International Journal of Aquaculture, 15(4): 175-183. Huang Y., and Khabusi S., 2025, Artificial intelligence of things (AIoT) advances in aquaculture: A review, Processes, 13(1): 73.
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