DOI: 10.12688/f1000research.185926.1 ISSN: 2046-1402

Artificial Intelligence in Aquaculture: Integrating Bibliometric Analysis and Science Mapping to Uncover Two Decades of Scientific Evolution and Future Research Agendas

Yulianti Anjarsari, Saniati Goa, Gloria Hillary Kesaulya, Anita Ogara, Siti Murni Zega, Chrismenda Erika Pay, Graciela Ivania Peea, Samsuri Djamal, Kharisma Arethusa Maisaroh
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Background Artificial Intelligence (AI) is transforming aquaculture through data-driven production management, environmental monitoring, and intelligent decision support. Despite growing scholarly interest, current knowledge remains fragmented across disciplines and application domains, limiting a comprehensive understanding of the field’s scientific evolution. This study provides an integrated assessment of the development, intellectual structure, and future trajectory of Artificial Intelligence in Aquaculture over the last two decades. Methods A bibliometric and science-mapping approach was applied to 98 Scopus-indexed publications (2005–2025). Bibliometric performance analysis was combined with logistic growth modelling, co-authorship, co-citation, and keyword co-occurrence analyses to examine research maturity, collaboration structures, intellectual foundations, and conceptual evolution. Results Scientific production has expanded rapidly and is approaching a phase of consolidation, while collaboration networks have become increasingly interconnected across countries and institutions. Research activity is concentrated within a limited number of influential contributors, with Asia emerging as the principal centre of scientific production. The field has evolved from methodological exploration towards an integrated digital ecosystem that combines artificial intelligence with environmental monitoring, automation, and sustainable aquaculture management. Building on these findings, this study proposes a co-evolutionary framework, demonstrating that scientific advancement is driven by the interaction between collaboration networks, intellectual consolidation, and conceptual transformation rather than publication growth alone. Future progress is expected to depend on trustworthy AI, interoperable digital infrastructures, and multidisciplinary collaboration capable of supporting resilient and sustainable smart aquaculture .

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