Freshwater Monitoring System Design In Real-Time For Fish Cultivation

Affan Bachri

doi:10.59653/ijmars.v2i01.483

Authors

Affan Bachri Universitas Islam Lamongan

DOI:

https://doi.org/10.59653/ijmars.v2i01.483

Keywords:

IoT (Internet of Things), Water Quality, Fuzzy Logic, Real-Time Monitoring

Abstract

The increased demand for fish supply in aquaculture highlights the need for sophisticated and accurate monitoring systems to ensure optimal water quality. In this context, Internet of Things (IoT) technology and fuzzy logic have become promising solutions to improve efficiency and effectiveness in freshwater fish farming. This research aims to develop a real-time freshwater monitoring system that integrates IoT and fuzzy logic. This system will enable monitoring of critical parameters such as temperature, pH and water turbidity with a high degree of accuracy. Implementation of IoT sensors that are connected to one centralized network and use fuzzy logic to process the data obtained. The research also involved developing an intuitive user interface to manage the system. The developed system is able to provide real-time monitoring with a high level of accuracy. Users can easily access and analyze the generated data through the user interface provided. The design of monitoring freshwater conditions for intensive aquaculture can be prepared using pH sensor devices, turbidity sensors, and temperature sensors using ESP2866 modules to connect to IoT. From the results of 20 days of system trials, it was found that the system can determine the condition of fresh water with good readings with average level of accuracy for pH sensors of 97%, turbidity sensors of 92% and temperature sensors of 96%.

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References

Agustin, M., Zain, A. R., Soelaiman, N. F., Oktivasari, P., Bohan, J. R., Karim, M. A., Anshor, F., & Fahroji, M. F. (2022). The Aquarium Monitoring System Design and Prototype for Ornamental Fish Farmers using NodeMCU with Telegram Data Notifications. 162–166.

Anggara Trisna Nugraha, & Priyambodo, D. (2020). Design of Pond Water Turbidity Monitoring System in Arduino-based Catfish Cultivation to Support Sustainable Development Goals 2030 No.9 Industry, Innovation, and Infrastructure. Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2(3), 119–124. https://doi.org/10.35882/jeeemi.v2i3.6

Avnimelech, Y. (2007). Feeding with microbial flocs by tilapia in minimal discharge bio-flocs technology ponds. Aquaculture, 264(1–4), 140–147. https://doi.org/10.1016/j.aquaculture.2006.11.025

Borges, W., Junior, L., Nunes, R. M., Andrade, L. De, Cordeiro, Í., Lima, S., Fiuza, L. S., & Dias, M. (2019). Development of a Low-Cost System for Monitoring Water Quality applied to Fish Culture. 6495(7), 1–5.

Budiman, F., Rivai, M., & Nugroho, M. A. (2019). Monitoring and Control System for Ammonia and pH Levels for Fish Cultivation Implemented on Raspberry Pi 3B. Proceedings - 2019 International Seminar on Intelligent Technology and Its Application, ISITIA 2019, 68–73. https://doi.org/10.1109/ISITIA.2019.8937217

Dela Cruz, J. R., Magsumbol, J. A. V., Dadios, E. P., Baldovino, R. G., Culibrina, F. B., & Lim, L. A. G. (2017). Design of a fuzzy-based automated organic irrigation system for smart farm. HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, 2018-Janua, 1–6. https://doi.org/10.1109/HNICEM.2017.8269500

Fakhrurroja, H., Mardhotillah, S. A., Mahendra, O., Munandar, A., Rizqyawan, M. I., & Pratama, R. P. (2019). Automatic pH and Humidity Control System for Hydroponics Using Fuzzy Logic. 2019 International Conference on Computer, Control, Informatics and Its Applications: Emerging Trends in Big Data and Artificial Intelligence, IC3INA 2019, 156–161. https://doi.org/10.1109/IC3INA48034.2019.8949590

FAO Fisheries and Aquaculture Department. (2022). The State of World Fisheries and Aquaculture 2022.Towards Blue Transformation. In In Brief to The State of World Fisheries and Aquaculture 2022. https://doi.org/10.4060/cc0463en

Ginanjar, P., Opipah, S., Rusmana, D., Muhlas, Effendi, M. R., & Hamidi, E. A. Z. (2021). Prototype Smart Fish Farm in Koi Fish Farming. Proceeding of 2021 7th International Conference on Wireless and Telematics, ICWT 2021, 0–5. https://doi.org/10.1109/ICWT52862.2021.9678208

Haiyunnisa, M. T. (2016). Water Aquaculture Based Fuzzy Logic : MATLAB Based Simulation Approach. 1–5.

Haiyunnisa, T., Alam, H. S., & Salim, T. I. (2017). Design and implementation of fuzzy logic control system for water quality control. Proceedings of the 2nd International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology, ICACOMIT 2017, 2018-Janua, 98–102. https://doi.org/10.1109/ICACOMIT.2017.8253394

Hapsari, G. I., Wakid, Z., & Mudopar, S. (2020). IoT-based guppy fish farming monitoring and controlling system. 18(3), 1538–1545. https://doi.org/10.12928/TELKOMNIKA.v18i3.14850

Jijin, C. K., & Roshith, K. (2016). ONLINE MONITORING AND CONTROL AQUAPONICS USING LAB VIEW. 23–27.

Khaoula, T., Abdelouahid, R. A., Ezzahoui, I., & Marzak, A. (2021). Architecture design of monitoring and controlling of IoT-based aquaponics system powered by solar energy. Procedia Computer Science, 191, 493–498. https://doi.org/10.1016/j.procs.2021.07.063

Kyaw, T. Y., & Ng, A. K. (2017). ScienceDirect ScienceDirect ScienceDirect The 15th Aquaponics International Symposium System Urban Thu Ya Kyaw of Assessing the feasibility using Keong the heat function for a long-term district demand forecast. Energy Procedia, 143, 342–347. https://doi.org/10.1016/j.egypro.2017.12.694

Prasad, M., Majeed, S., Romichan, S., Mathew, W., & Udaybabu, P. (2020). Cost Effective IoT based Automated Fish Farming System with Flood Prediction. 9(1), 291–297.

Ramadhona, M. D., & Hakim, D. L. (2018). System of Water Quality Monitoring and Feeding on Freshwater Fish Cultivation. IOP Conference Series: Materials Science and Engineering, 384(1). https://doi.org/10.1088/1757-899X/384/1/012034

Robert R. Stickney. (2005). Aquaculture: An Introductory Text (S. G. C. P. T. A. & M. U. Usa (ed.)). Cabi Publishing-Sea Grant College Programme Texas A & M University.

Shen, X., Chen, M., & Yu, J. (2009). Water environment monitoring system based on neural networks for shrimp cultivation. 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, 3, 427–431. https://doi.org/10.1109/AICI.2009.294

Sneha, P. S., & Rakesh, V. S. (2018). Automatic monitoring and control of shrimp aquaculture and paddy field based on embedded system and IoT. Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017, Icici, 1085–1089. https://doi.org/10.1109/ICICI.2017.8365307

Wijaya, D. P., Elfitasari, T., & Sarjito. (2016). ANALISA PROSPEK BISNIS BUDIDAYA PEMBESARAN IKAN BANDENG (Chanos chanos) DI KECAMATAN TUGU KOTA SEMARANG. Journal of Aquaculture Management and Technology, 5(3).