Internet of Things-Based Water Quality Monitoring and Automatic Feeding for Catfish Ponds Using Mamdani Fuzzy Logic
DOI:
https://doi.org/10.30865/jurikom.v13i3.9857Keywords:
Water Quality Monitoring, Automatic Fish Feeding, Fuzzy Logic, Internet of Things, Real-Time Monitoring and ControlAbstract
Manual water-quality monitoring and schedule-based feeding may delay responses to unsuitable pond conditions and lead to inappropriate feed dispensing in catfish cultivation. This study developed an Internet of Things (IoT)-based monitoring and automatic feeding system that evaluates water temperature and pH using Mamdani fuzzy inference. The prototype integrates an ESP32 microcontroller, temperature and pH sensors, an ultrasonic sensor for feed-level monitoring, and a servo motor for feed dispensing. Temperature and pH measurements are classified into five linguistic categories and evaluated through a 25-rule base to produce a binary Feed ON or Feed OFF decision. Sensor validation yielded accuracies of 95.52% for temperature measurement, 95.87% for pH measurement, and 95.97% for ultrasonic distance measurement. Validation using five representative input combinations showed that all program outputs matched the expected rule-base decisions, resulting in 100% decision conformity for the tested cases. During seven days of field monitoring, all 21 evaluations conducted at 09:00, 15:00, and 21:00 produced Feed ON because the paired temperature and pH measurements satisfied the minimum rule-base requirements. Web and mobile dashboards displayed sensor measurements, feed availability, device status, operating mode, and feeding activity in real time. The main contribution is a compact and interpretable mechanism that performs condition-based feeding decisions locally on the ESP32 while supporting remote supervision through IoT interfaces. The results indicate adequate sensor performance and consistent fuzzy decision implementation under the tested conditions.
References
[1]
Food and Agriculture Organization of the United Nations, The State of World Fisheries and Aquaculture 2024: Blue Transformation in Action. Rome, Italy: FAO, 2024, doi: 10.4060/cd0683en.
[2] Ministry of Agriculture, Animal Industry and Fisheries, Aquaculture Training Manual for Extension Agents in Uganda: Improved Livelihoods through Profitable, Competitive and Sustainable Aquaculture. Entebbe, Uganda: MAAIF, 2020.
[3] Z. Ariyandi, T. Taufiq, and N. Nunsina, “Design of an Internet of Things (IoT)-based fish feeder system using an Android application,” Journal of Applied Informatics and Computing, vol. 9, no. 4, pp. 1593–1601, 2025, doi: 10.30871/jaic.v9i4.10072.
[4] 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, Art. no. 6700, 2022, doi: 10.3390/s22176700.
[5] R. P. Shete, A. M. Bongale, and D. Dharrao, “IoT-enabled effective real-time water quality monitoring method for aquaculture,” MethodsX, vol. 13, Art. no. 102906, 2024, doi: 10.1016/j.mex.2024.102906.
[6] S. K. Nagothu, P. Bindu Sri, G. Anitha, S. Vincent, and O. P. Kumar, “Advancing aquaculture: Fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture,” Aquaculture International, vol. 33, no. 1, Art. no. 32, 2025, doi: 10.1007/s10499-024-01701-2.
[7] A. Prafanto, A. Septiarini, N. Puspitasari, M. Taruk, and D. A. Mahendra, “IoT-based water quality control in tilapia aquaculture using fuzzy logic,” Innovation in Research of Informatics (INNOVATICS), vol. 6, no. 2, pp. 57–64, 2024, doi: 10.37058/innovatics.v6i2.11271.
[8] Sugiarto, I. Nugraha, T. M. Fahrudin, A. Rizqina, and K. Agvenia, “IoT-based water quality monitoring system to enhance sustainability and business performance in koi fish cultivation,” Journal of Advances in Information and Industrial Technology, vol. 7, no. 2, pp. 193–206, 2025, doi: 10.52435/jaiit.v7i2.730.
[9] A. Subowo and N. Pradita, “Design of an IoT-based aquaculture monitoring system based on IoT in fish farming,” Energy: Jurnal Ilmiah Ilmu-Ilmu Teknik, vol. 15, no. 2, pp. 152–164, 2025, doi: 10.51747/energy.v15i2.15203.
[10] F. Aljehani, I. N’Doye, and T.-M. Laleg-Kirati, “Feeding control and water quality monitoring on bioenergetic fish growth modeling: Opportunities and challenges,” Aquacultural Engineering, vol. 109, Art. no. 102511, 2025, doi: 10.1016/j.aquaeng.2024.102511.
[11] M. A. Adegboye, A. M. Aibinu, J. G. Kolo, I. Aliyu, T. A. Folorunso, and S.-H. Lee, “Incorporating intelligence in fish feeding system for dispensing feed based on fish feeding intensity,” IEEE Access, vol. 8, pp. 91948–91960, 2020, doi: 10.1109/ACCESS.2020.2994442.
[12] M. Alselek, J. M. Alcaraz-Calero, J. Segura-Garcia, and Q. Wang, “Water IoT monitoring system for aquaponics health and fishery applications,” Sensors, vol. 22, no. 19, Art. no. 7679, 2022, doi: 10.3390/s22197679.
[13] H.-Y. Lu et al., “A low-cost AI buoy system for monitoring water quality at offshore aquaculture cages,” Sensors, vol. 22, no. 11, Art. no. 4078, 2022, doi: 10.3390/s22114078.
[14] W.-T. Sung, I. G. T. Isa, and S.-J. Hsiao, “An IoT-based aquaculture monitoring system using Firebase,” Computers, Materials & Continua, vol. 76, no. 2, pp. 2179–2200, 2023, doi: 10.32604/cmc.2023.041022.
[15] W.-T. Sung, I. G. T. Isa, and S.-J. Hsiao, “Designing aquaculture monitoring system based on data fusion through deep reinforcement learning (DRL),” Electronics, vol. 12, no. 9, Art. no. 2032, 2023, doi: 10.3390/electronics12092032.
[16] M. Flores-Iwasaki, G. A. Guadalupe, M. Pachas-Caycho, S. Chapa-Gonza, R. C. Mori-Zabarburú, and J. C. Guerrero-Abad, “Internet of Things (IoT) sensors for water quality monitoring in aquaculture systems: A systematic review and bibliometric analysis,” AgriEngineering, vol. 7, no. 3, Art. no. 78, 2025, doi: 10.3390/agriengineering7030078.
[17] F. Akhter, H. R. Siddiquei, M. E. E. Alahi, and S. C. Mukhopadhyay, “Recent advancement of the sensors for monitoring the water quality parameters in smart fisheries farming,” Computers, vol. 10, no. 3, Art. no. 26, 2021, doi: 10.3390/computers10030026.
[18] Y. Singh and T. Walingo, “Smart water quality monitoring with IoT wireless sensor networks,” Sensors, vol. 24, no. 9, Art. no. 2871, 2024, doi: 10.3390/s24092871.
[19] B. Mahalder, M. M. Haque, M. A. B. Siddique, N. A. Hasan, M. M. Alam, M. M. N. Talukdar, M. H. Shohan, N. Ahasan, M. M. Hasan, and A. K. S. Ahammad, “Embryonic and larval development of stinging catfish, Heteropneustes fossilis, in relation to climatic and water quality parameters,” Life, vol. 13, no. 2, Art. no. 583, 2023, doi: 10.3390/life13020583.
[20] B. Nchegang, T. M. Enow, G. O. Nkongho, and P. V. Tan, “Impact of water quality on the growth performance of Clarias gariepinus in fish farms within Fako Division, Cameroon,” Asian Journal of Fisheries and Aquatic Research, vol. 26, no. 7, pp. 98–107, 2024, doi: 10.9734/ajfar/2024/v26i7787.
[21]
A. M. Gadallah, S. A. Elsayed, S. Mousa, and H. A. Hefny, “A fuzzy-based approach for flexible modeling and management of freshwater fish farming,” Mathematics, vol. 12, no. 13, Art. no. 2146, 2024, doi: 10.3390/math12132146.
[22]
F. Bellini, Y. Barzegar, A. Barzegar, S. Marrone, L. Verde, and P. Pisani, “Sustainable water quality evaluation based on cohesive Mamdani and Sugeno fuzzy inference system in Tivoli (Italy),” Sustainability, vol. 17, no. 2, Art. no. 579, 2025, doi: 10.3390/su17020579.
