IoT-Based Precision Aquaculture: A Bibliometric Analysis of Research Trends from 2020 to 2026 - Anjaini, Jefri; Fadhillah, Radhi; Hidayat, Rahmat; Laily, Farkhana; Priyono, Susilo Budi; Sujadi, Frentina Murti; Affandi, Rangga Idris (Jurnal Biologi Tropis, 2026)
The adoption of the Internet of Things (IoT) has transformed aquaculture from manual monitoring into data-driven precision management. This study maps the intellectual structure, publication trends, key contributors, and emerging research directions in IoT-based precision aquaculture, spanning sensor-based monitoring to decision support systems. A bibliometric analysis was conducted on 303 Scopus-indexed documents retrieved using a TITLE-ABS-KEY query combining “Internet of Things” and related terms with “aquaculture.” Document screening followed the PRISMA 2020 protocol. Performance analysis and science mapping were performed using VOSviewer through citation-network and keyword co-occurrence analyses. Because data retrieval occurred during the partial publication year 2026, the compound annual growth rate (CAGR = 19.5%) was calculated only for the complete-year period 2020–2025, while 2026 publications were included in citation and mapping analyses. The dataset accumulated 4,791 citations (mean = 15.81 citations per document; h-index = 38). Sensors was the most productive journal, whereas Indonesia and India were the leading publishing countries. Keyword co-occurrence identified five major research themes: sensor-based water-parameter monitoring, environmental quality monitoring, IoT–machine learning integration, smart fish farming, and AI-based decision support. These findings indicate a shift from environmental monitoring toward intelligent, predictive, and decision-oriented aquaculture management.