IoT-Based Precision Aquaculture: A Bibliometric Analysis of Research Trends from 2020 to 2026
DOI:
10.29303/jbt.v26i3.12602Published:
2026-07-28Downloads
Abstract
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.
Keywords:
Bibliometric Analysis Decision Support System Internet of Things Precision Aquaculture Vosviewer Water Quality MonitoringReferences
Abdurohman, M., Putrada, A. G., & Deris, M. M. (2022). A robust Internet of Things-based aquarium control system using decision tree regression algorithm. IEEE Access, 10, 56937–56951. https://doi.org/10.1109/ACCESS.2022.3177225
Al Rasyid, M. U. H., Sukaridhoto, S., Dzulqornain, M. I., & Rifa'i, A. (2020). Integration of IoT and chatbot for aquaculture with natural language processing. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(2), 640–648. https://doi.org/10.12928/TELKOMNIKA.V18I2.14788
Arepalli, P. G., & Jairam Naik, K. (2024). An IoT based smart water quality assessment framework for aqua-ponds management using Dilated Spatial-temporal Convolution Neural Network (DSTCNN). Aquacultural Engineering, 104, 102373. https://doi.org/10.1016/j.aquaeng.2023.102373
Arepalli, P. G., & Naik, K. J. (2024). A deep learning-enabled IoT framework for early hypoxia detection in aqua water using lightweight spatially shared attention-LSTM network. The Journal of Supercomputing, 80(2), 2718–2747. https://doi.org/10.1007/s11227-023-05580-x
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Bates, H., Pierce, M., & Benter, A. (2021). Real-time environmental monitoring for aquaculture using a LoRaWAN-based IoT sensor network. Sensors, 21(23), 7963. https://doi.org/10.3390/s21237963
Chen, C.-H., Wu, Y.-C., Zhang, J.-X., & Chen, Y.-H. (2022). IoT-based fish farm water quality monitoring system. Sensors, 22(17), 6700. https://doi.org/10.3390/s22176700
Chiu, M.-C., Yan, W.-M., Bhat, S. A., & Huang, N.-F. (2022). Development of smart aquaculture farm management system using IoT and AI-based surrogate models. Journal of Agriculture and Food Research, 9, 100357. https://doi.org/10.1016/j.jafr.2022.100357
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
FAO. (2024). The State of World Fisheries and Aquaculture 2024 – Blue Transformation in action. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cd0683en
Gambin, A. F., Angelats, E., González, J. S., Miozzo, M., & Dini, P. (2021). Sustainable marine ecosystems: Deep learning for water quality assessment and forecasting. IEEE Access, 9, 121344–121365. https://doi.org/10.1109/ACCESS.2021.3109216
Hang, L., Ullah, I., & Kim, D.-H. (2020). A secure fish farm platform based on blockchain for agriculture data integrity. Computers and Electronics in Agriculture, 170, 105251. https://doi.org/10.1016/j.compag.2020.105251
Huan, J., Li, H., Wu, F., & Cao, W. (2020). Design of water quality monitoring system for aquaculture ponds based on NB-IoT. Aquacultural Engineering, 90, 102088. https://doi.org/10.1016/j.aquaeng.2020.102088
Jamroen, C., Yonsiri, N., Odthon, T., Wisitthiwong, N., & Janreung, S. (2023). A standalone photovoltaic/battery energy-powered water quality monitoring system based on narrowband Internet of Things for aquaculture: Design and implementation. Smart Agricultural Technology, 3, 100072. https://doi.org/10.1016/j.atech.2022.100072
Khuen Cheng, W., Boon Tan, T., San Tan, J., Yi-Le Chan, J., Thye Bea, K., & Chen, Y.-L. (2025). Smart aquaculture: Integrating computer vision and IoT for automated crayfish health assessment via federated learning. IEEE Access, 13, 189979–189994. https://doi.org/10.1109/ACCESS.2025.3624140
Lin, J.-Y., Tsai, H.-L., & Lyu, W.-H. (2021). An integrated wireless multi-sensor system for monitoring the water quality of aquaculture. Sensors, 21(24), 8179. https://doi.org/10.3390/s21248179
Manan, H., Jalilah, M., Fauzan, F., Ikhwanuddin, M., Amin-Safwan, A., Abdullah, N. S., Nur-Syahirah, M., & Kasan, N. A. (2023). Recent developments in aquaculture – A review. Annals of Animal Science, 23(3), 663–684. https://doi.org/10.2478/aoas-2023-0044
Nagothu, S. K., Bindu Sri, P., Anitha, G., Vincent, S., & Kumar, O. P. (2025). Advancing aquaculture: Fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture. Aquaculture International, 33(1), 32. https://doi.org/10.1007/s10499-024-01701-2
O'Donncha, F., Stockwell, C. L., Planellas, S. R., Micallef, G., Palmes, P., Webb, C., Filgueira, R., & Grant, J. (2021). Data driven insight into fish behaviour and their use for precision aquaculture. Frontiers in Animal Science, 2, 695054. https://doi.org/10.3389/fanim.2021.695054
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Riftiarrasyid, M. F., & Soewito, B. (2024). Monitoring water quality parameters impacted by Indonesia's weather using Internet of Things. Indonesian Journal of Electrical Engineering and Computer Science, 35(3), 1426–1436. https://doi.org/10.11591/ijeecs.v35.i3.pp1426-1436
Rojas, J. J. H., Bonifacio, R. F. A., Peña, J. M. B., León, D. R. Q., Peña, C. D. R., & Torres, K. G. P. (2025). Sensors and IoT for water quality monitoring: A systematic review of technologies and field validation. Journal of Robotics and Control (JRC), 6(6), 2789–2806. https://doi.org/10.18196/jrc.v6i6.28457
Satra, R., Hadi, M. S., Sujito, Febryan, Fattah, M. H., & Busaeri, S. R. (2024). IoAT: Internet of Aquaculture Things for monitoring water temperature in tiger shrimp ponds with DS18B20 sensors and WeMos D1 R2. Journal of Robotics and Control (JRC), 5(1), 62–71. https://doi.org/10.18196/jrc.v5i1.18470
Shete, R. P., Shekhar, C. A., Mahajan, Y. V., Bongale, A. M., & Dharrao, D. (2025). IoT-driven ensemble machine learning model for accurate dissolved oxygen prediction in aquaculture. Discover Internet of Things, 5(1), 94. https://doi.org/10.1007/s43926-025-00201-w
Sung, W.-T., Isa, I. G. T., & Hsiao, S.-J. (2023). Designing aquaculture monitoring system based on data fusion through deep reinforcement learning (DRL). Electronics, 12(9), 2032. https://doi.org/10.3390/electronics12092032
Thai-Nghe, N., Thanh-Hai, N., & Ngon, N. C. (2020). Deep learning approach for forecasting water quality in IoT systems. International Journal of Advanced Computer Science and Applications, 11(8), 686–693. https://doi.org/10.14569/IJACSA.2020.0110883
Ubina, N. A., Lan, H.-Y., Cheng, S.-C., Chang, C.-C., Lin, S.-S., Zhang, K.-X., Lu, H.-Y., Cheng, C.-Y., & Hsieh, Y.-Z. (2023). Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT). Smart Agricultural Technology, 5, 100285. https://doi.org/10.1016/j.atech.2023.100285
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Wang, J.-H., Lee, S.-K., Lai, Y.-C., Lin, C.-C., Wang, T.-Y., Lin, Y.-R., Hsu, T.-H., Huang, C.-W., & Chiang, C.-P. (2020). Anomalous behaviors detection for underwater fish using AI techniques. IEEE Access, 8, 224372–224382. https://doi.org/10.1109/ACCESS.2020.3043712
Yang, C.-T., Chen, H.-W., Chang, E.-J., Kristiani, E., Nguyen, K. L. P., & Chang, J.-S. (2021). Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control. Journal of Hazardous Materials, 419, 126442. https://doi.org/10.1016/j.jhazmat.2021.126442
Yang, L., Liu, Y., Yu, H., Fang, X., Song, L., Li, D., & Chen, Y. (2021). Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: A review. Archives of Computational Methods in Engineering, 28(4), 2785–2816. https://doi.org/10.1007/s11831-020-09486-2
Zhang, Y., Liu, Y., Jiong, Z., Zhang, X., Li, B., & Chen, E. (2021). Development and assessment of blockchain-IoT-based traceability system for frozen aquatic product. Journal of Food Process Engineering, 44(5), e13669. https://doi.org/10.1111/jfpe.13669.
License
Copyright (c) 2026 Jefri Anjaini, Radhi Fadhillah, Rahmat Hidayat, Farkhana Laily, Susilo Budi Priyono, Frentina Murti Sujadi, Rangga Idris Affandi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Jurnal Biologi Tropis is licensed under a Creative Commons Attribution 4.0 International License.
The copyright of the received article shall be assigned to the author as the owner of the paper. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to the published articles.
Authors are permitted to disseminate published articles by sharing the link/DOI of the article at the journal. Authors are allowed to use their articles for any legal purposes deemed necessary without written permission from the journal with an acknowledgment of initial publication to this journal.
