Open Access
| Issue |
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
|
|
|---|---|---|
| Article Number | 03009 | |
| Number of page(s) | 20 | |
| Section | Smart and Sustainable Systems | |
| DOI | https://doi.org/10.1051/epjconf/202636703009 | |
| Published online | 29 April 2026 | |
- M. F. Ilmi, S. Syahrorini, S. D. Ayuni, Automatic water quality and fish feed monitoring system in aquarium using LORA. J. Comput. Networks Archit. High Perform. Comput. 5(2), 444–457 (2023) [Google Scholar]
- M. Amrita, B. Dharmadhas, S. Nivetha, P. Sivanesan, IoT based smart water quality management in aquarium. SSRN Electron. J. 0–4 (2021) [Google Scholar]
- D. Dhinakaran, S. Gopalakrishnan, M. D. Manigandan, T. P. Anish, IoT-based environmental control system for fish farms with sensor integration and machine learning decision support. arXiv preprint arXiv:2311.04258 (2023) [Google Scholar]
- X. Yang, S. Zhang, J. Liu, Q. Gao, S. Dong, C. Zhou, Deep learning for smart fish farming: applications, opportunities and challenges. Rev. Aquacult. 13(1), 66–90 (2021) [Google Scholar]
- M. M. Hemal, A. Rahman, F. Islam, S. Ahmed, M. S. Kaiser, M. R. Ahmed, An integrated smart pond water quality monitoring and fish farming recommendation aquabot system. Sensors 24(11), 3682 (2024) [Google Scholar]
- S. R. Gokulnath, K. Vasanthakumaran, A. Thanga Anusya, T. Paul Nathaniel, S. K. Naveen, J. S. Akash, S. Abuthagir Ibrahim, Precision aquaculture: empowering fish farming with AI and IoT (2023) [Google Scholar]
- K. S. K. Patro, V. K. Yadav, V. S. Bharti, A. Sharma, A. Sharma, T. Senthilkumar, IoT and ML approach for ornamental fish behaviour analysis. Sci. Rep. 13(1), 21415 (2023) [Google Scholar]
- M. C. Chiu, W. M. Yan, S. A. Bhat, N. F. Huang, Development of smart aquaculture farm management system using IoT and AI-based surrogate models. J. Agric. Food Res. 9, 100357 (2022) [Google Scholar]
- P. G. Lee, A review of automated control systems for aquaculture and design criteria for their implementation. Aquacult. Eng. 14(3), 205–227 (1995) [Google Scholar]
- S. M. Petrea, A. C. Bandi, D. Cristea, M. Neculiță, Cost-benefit analysis into integrated aquaponics systems. Custos e Agronegocio 15(3), 239–269 (2019) [Google Scholar]
- R. Henkel, B. H. Buck, B. Cembella, R. Fisch, O. Zielinski, Implementation of a sensor suite for an aquaculture plant using chemical-reagent-free measuring methods. OCEANS 2007-Europe 1–4 (2007) [Google Scholar]
- B. E. Elmurodova, M. S. Yakubov, S. S. Beknazarova, Model of automation of growth carp fish pond warm-water economy. Int. Conf. Inf. Sci. Commun. Technol. 1–5 (2021) [Google Scholar]
- N. A. Ubina, S. C. Cheng, A review of unmanned system technologies with its application to aquaculture farm monitoring and management. Drones 6(1), 12 (2022) [CrossRef] [Google Scholar]
- W. T. Sung, J. H. Chen, H. C. Wang, Remote fish aquaculture monitoring system based on wireless transmission technology. Int. Conf. Inf. Sci. Electron. Electr. Eng. 1, 540–544 (2014) [Google Scholar]
- C. Lee, H. Park, J. Kim, Smart aquaponics: a data-driven approach for sustainable fish farming. J. Smart Agric. 8(1), 77–89 (2024) [Google Scholar]
- D. Patel, K. Verma, Real-time IoT-based water quality monitoring for fish hatcheries. Int. J. Aquat. Res. 15(4), 213–227 (2023) [Google Scholar]
- L. Wang, H. Zhou, X. Liu, Machine learning in precision aquaculture: a review of applications and challenges. Comput. Aquacult. 10(2), 99–113 (2022) [Google Scholar]
- P. Fernandez, M. Gonzales, Wireless sensor networks for water quality monitoring in fish farms. IEEE Trans. Aquacult. Syst. 5(6), 125–138 (2023) [Google Scholar]
- J. Park, D. Choi, AI-based anomaly detection for smart aquaculture systems. IEEE J. Smart Aquat. Environ. 6(3), 145–159 (2024) [Google Scholar]
- H. Wu, Y. Lin, Predictive maintenance in aquaculture using machine learning. Smart Agric. Aquacult. 7(4), 217–233 (2022) [Google Scholar]
- M. Johnson, S. Lee, Enhancing water quality monitoring in fish hatcheries using IoT and cloud computing. J. Aquacult. Technol. 11(2), 101–115 (2023) [Google Scholar]
- T. Kim, C. Yang, Edge computing in IoT-based aquaculture monitoring systems. Comput. Sustain. Agric. 10(2), 67–81 (2024) [Google Scholar]
- L. Fernandez, J. Roberts, Remote monitoring and automation in precision aquaculture. IEEE Trans. Agric. IoT 8(5), 299–314 (2024) [Google Scholar]
- Siswanto, B. Mardiyana, A simple dataset of aquaponic fish pond IoT. Kaggle Dataset (2023). https://doi.org/10.34740/KAGGLE/DSV/522546 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

