IoT-based Water Quality Control in Tilapia Aquaculture Using Fuzzy Logic

Anton Prafanto, Anindita Septiarini, Novianti Puspitasari, Medi Taruk, Dicky Alvian Mahendra

Abstract

Tilapia (Oreochromis niloticus) is a prominent species in freshwater aquaculture due to its high protein content and economic value. Maintaining optimal water quality is crucial for the health and growth of tilapia, particularly in terms of pH levels. Deviations in pH, whether too acidic or too alkaline, can lead to decreased appetite and increased mortality rates in tilapia. The objective of this study is to design an intelligent control system to monitor and regulate the pH and temperature of tilapia aquaculture ponds using the Sugeno Fuzzy method integrated with Internet of Things (IoT) technology. The system employs DS18B20 temperature sensors and E-201-C pH sensors to collect real-time data on pond conditions. The data are then processed by an ESP32 microcontroller, which employs Sugeno Fuzzy logic to determine the appropriate adjustments to be made. The system administers pH buffers to maintain the water within the optimal pH range. Furthermore, the collected data are transmitted to a web server, enabling real-time monitoring and analysis. The findings indicate that the proposed IoT-based system is effective in maintaining water quality, ensuring that the pH and temperature levels remain within the ideal range for tilapia. This study demonstrates the potential of integrating IoT and Sugeno Fuzzy logic to provide a robust solution for managing water quality in aquaculture settings, enhancing the sustainability and productivity of tilapia farming.

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References

N. Mashaii et al., “Reproductive Biology of Nile Tilapia, Oreochromis niloticus under the Brackish Water Culture Condition,” International Journal of Food Science and Agriculture, vol. 6, no. 1, pp. 4–7, Jan. 2022, doi: 10.26855/ijfsa.2022.03.002.

A. W. S. Martins et al., “Exposure to salinity induces oxidative damage and changes in the expression of genes related to appetite regulation in Nile tilapia (Oreochromis niloticus),” Front Genet, vol. 13, Sep. 2022, doi: 10.3389/fgene.2022.948228.

Y. Chang, J. Xu, and Z. Ghafoor, “AN IOT AND BLOCKCHAIN APPROACH FOR THE SMART WATER MANAGEMENT SYSTEM IN AGRICULTURE,” vol. 22, no. 2, pp. 105–116, 2021, doi: 10.12694:/scpe.v22i2.1869.

M. M. Islam, M. A. Kashem, and J. Uddin, “An internet of things framework for real-time aquatic environment monitoring using an Arduino and sensors,” International Journal of Electrical and Computer Engineering, vol. 12, no. 1, pp. 826–833, Feb. 2022, doi: 10.11591/ijece.v12i1.pp826-833.

F. Lezzar, D. Benmerzoug, and I. Kitouni, “IoT for monitoring and control of water quality parameters,” International Journal of Interactive Mobile Technologies, vol. 14, no. 16, pp. 4–19, 2020, doi: 10.3991/ijim.v14i16.15783.

S. Kambalimath and P. C. Deka, “A basic review of fuzzy logic applications in hydrology and water resources,” Applied Water Science, vol. 10, no. 8. Springer Science and Business Media Deutschland GmbH, Aug. 01, 2020. doi: 10.1007/s13201-020-01276-2.

L. Liu and H. Deng, “A fuzzy approach for ranking discrete multi-attribute alternatives under uncertainty,” Mathematics, vol. 8, no. 6, Jun. 2020, doi: 10.3390/MATH8060945.

L. K. Tolentino et al., “IoT-based automated water monitoring and correcting modular device via LoRaWAN for aquaculture,” International Journal of Computing and Digital Systems, vol. 10, no. 1, pp. 533–544, 2021, doi: 10.12785/IJCDS/100151.

G. You et al., “Evaluation of aquaculture water quality based on improved fuzzy comprehensive evaluation method,” Water (Switzerland), vol. 13, no. 8, Apr. 2021, doi: 10.3390/w13081019.

C. H. Chen, Y. C. Wu, J. X. Zhang, and Y. H. Chen, “IoT-Based Fish Farm Water Quality Monitoring System,” Sensors, vol. 22, no. 17, Sep. 2022, doi: 10.3390/s22176700.

E. Syrmos et al., “An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements,” Sustainability (Switzerland), vol. 15, no. 3, Feb. 2023, doi: 10.3390/su15032127.

M. Lowe, R. Qin, and X. Mao, “A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring,” Water (Switzerland), vol. 14, no. 9, May 2022, doi: 10.3390/w14091384.

F. Jan, N. Min-Allah, and D. Düştegör, “Iot based smart water quality monitoring: Recent techniques, trends and challenges for domestic applications,” Water (Switzerland), vol. 13, no. 13. MDPI AG, Jul. 01, 2021. doi: 10.3390/w13131729.

W. M. Sanya, M. A. Alawi, and I. Eugenio, “Design and development of Smart Water Quality Monitoring System Using IoT,” International Journal of Advances in Scientific Research and Engineering, vol. 08, no. 03, pp. 01–13, 2022, doi: 10.31695/ijasre.2022.8.3.1.

O. O. Olanubi, T. T. Akano, and O. S. Asaolu, “Design and development of an IoT-based intelligent water quality management system for aquaculture,” Journal of Electrical Systems and Information Technology, vol. 11, no. 1, Mar. 2024, doi: 10.1186/s43067-024-00139-z.

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