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 (Biology Education Study Program, Faculty of Teacher Training and Education, University of Mataram, Indonesia, 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.