Transformasi Digital Perikanan Berbasis Artificial Intelligence Untuk Mendukung Pengelolaan Sumber Daya Ikan Berkelanjutan
DOI:
https://doi.org/10.70134/peraut.v3i1.953Keywords:
Artificial Intelligence, Digital Transformation, Sustainable FisheriesAbstract
Digital transformation has become one of the key strategies for supporting sustainable fisheries resource management. This study aims to examine the role of Artificial Intelligence (AI) in improving efficiency, productivity, and effectiveness within the fisheries sector. The research employed a literature review approach by analyzing scientific publications, international organization reports, and related documents on AI applications in capture fisheries, aquaculture, and fisheries supply chains. The findings indicate that AI can enhance the accuracy of fish stock prediction, support the detection of Illegal, Unreported, and Unregulated (IUU) fishing activities, optimize aquaculture systems through automated feeding and disease detection, and improve the efficiency of fisheries product distribution. However, the implementation of AI still faces challenges, including limited digital infrastructure, high investment costs, and insufficient human resource capacity. Overall, AI has significant potential to become an innovative solution for developing a modern, efficient, and sustainable fisheries sector.
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References
Ahamed, F., & Rahman, M. (2021). Artificial intelligence applications in fisheries management. Journal of Marine Science, 45(2), 77–89.
Albalawi, Y., Wang, Q., & Wang, Z. (2020). Deep learning for fish species classification. Applied Computing and Informatics, 18(1), 45–56.
Allan, J. R., & Watson, J. E. (2022). Technology-driven conservation planning in marine ecosystems. Conservation Letters, 15(4), e12911.
Barbedo, J. G. A. (2019). Computer vision for fish farming: A systematic review. Aquacultural Engineering, 85, 1–12.
Brierley, A. (2021). Remote sensing in fisheries: Emerging tools and opportunities. Fish and Fisheries, 22(5), 876–891.
Chen, L., & Li, Z. (2023). Predictive analytics in aquaculture using machine learning models. Aquaculture Reports, 29, 101–119.
Costa, M., & D’Este, L. (2020). Automation technologies in modern aquaculture systems. Aquaculture International, 28(4), 1121–1137.
Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Dhar, M., & Chanda, A. (2022). AI-based fish detection for smart aquaculture. Computers and Electronics in Agriculture, 198, 107029.
Dodd, J., & Brown, C. (2021). Digital transformation of fisheries: Benefits and challenges. Marine Policy, 131, 104647.
FAO. (2022). The state of world fisheries and aquaculture 2022: Towards blue transformation. Food and Agriculture Organization of the United Nations.
Fang, J., & Cao, X. (2020). Machine learning models for biomass prediction in aquaculture. Aquaculture, 532, 735982.
Garcia, S. M., & Charles, A. T. (2022). Marine governance and technological innovation. ICES Journal of Marine Science, 79(6), 1571–1583.
Ghosh, A. (2021). Big data analytics for sustainable fisheries. Environmental Modelling & Software, 140, 105028.
Gupta, S., & Sharma, P. (2023). AI-enabled solutions for IUU fishing detection. Ocean & Coastal Management, 236, 106428.
Hossain, M. M., & Chen, W. (2020). Automatic feeding control systems in aquaculture. Aquacultural Engineering, 88, 102028.
Huang, Y., & Wang, S. (2022). Drone-based monitoring in coastal fisheries. Remote Sensing, 14(9), 1920.
Islam, M. A., & Begum, S. (2023). IoT-based environmental monitoring for aquaculture. Sensors, 23(1), 211.
Johnson, R., & Barry, C. (2021). AI ethics in marine resource management. Environmental Science & Policy, 126, 74–82.
Kim, H., & Lee, S. (2020). Blockchain for seafood traceability. Journal of Cleaner Production, 270, 122469.
Li, J., & Zhao, B. (2023). Enhanced fish schooling behavior analysis using machine learning. Ecological Informatics, 73, 102176.
Li, X., Chen, Y., Wang, H., & Zhang, Z. (2020). Artificial intelligence applications in smart aquaculture: A review. Aquaculture International, 28(6), 2525–2542.
Liu, Y., & Xu, P. (2022). Smart aquaculture systems: A review of current technologies. Aquaculture, 558, 738361.
Martin, S., & Hall, J. (2021). AI-assisted stock assessment in marine fisheries. Fisheries Research, 235, 105822.
Miller, A., & Grant, T. (2020). Digital fisheries governance in Southeast Asia. Marine Policy, 120, 104137.
Miller, A., & Tiller, R. (2020). Artificial intelligence and fisheries governance: Opportunities and challenges. Marine Policy, 117, 103954.
Nguyen, T., & Vo, D. (2023). Real-time fish tracking using deep neural networks. Expert Systems with Applications, 222, 119862.
Park, Y., & Oh, J. (2020). Edge computing in aquaculture environmental monitoring. Computers and Electronics in Agriculture, 178, 105766.
Patton, M. Q. (2015). Qualitative research and evaluation methods (4th ed.). SAGE Publications.
Rahman, M., & Kumar, A. (2022). Digital transformation and sustainable fisheries development. Sustainability, 14(18), 11452.
Salim, H., & Rahman, K. (2022). Satellite data for monitoring illegal fishing. Frontiers in Marine Science, 9, 926441.
Sarker, M. A., & Karim, M. F. (2021). AI-based decision support systems for fisheries. Journal of Ocean Technology, 16(2), 51–67.
Schaefer, K., Johnson, P., & Miller, T. (2021). Big data and artificial intelligence in fisheries management. Fisheries Science, 87(4), 567–580.
Tan, Z., & Wen, H. (2020). Mobile applications for digital fishery data collection. Information Processing in Agriculture, 7(4), 533–543.
Wang, Q., & Zhang, Y. (2022). AI-driven forecasting of water quality in aquaculture systems. Ecological Engineering, 181, 106726.
Yamada, T., & Okamoto, S. (2021). Automation in offshore aquaculture cages. Engineering in Agriculture, 9(3), 148–162.
Zhang, H., & Sun, P. (2023). Deep learning for underwater video analytics. IEEE Access, 11, 54321–54334.
Zhang, Y., Li, X., Chen, H., & Wang, J. (2021). Machine learning approaches for sustainable fisheries management. Fisheries Research, 241, 105996.
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