Automatic Pellet Dispenser with Water Quality and Oxygenation Monitoring using Hybrid Rule-Based Scheduling and Threshold Control Algorithm
DOI:
https://doi.org/10.54536/ajaset.v10i2.7716Keywords:
Aquaculture Automation, Hybrid Rule-Based Scheduling, Shrimp Farming, Threshold Control Algorithm, Water Quality MonitoringAbstract
The purpose of this study was to undertake the design and development of an automatic pellet dispenser that has an inbuilt water quality and oxygenation checks that can be used with shrimp aquaculture to solve the challenges of manual feeding, irregular water analysis and inability to control automated aeration levels. The work includes automation of feeding times, monitoring of five important water parameters (dissolved oxygen, pH, water level, temperature, and total dissolved solids) and automatic oxygenation via a hybrid rule-based scheduling and threshold control algorithm on a dual controller system. This system was designed based on a Heltec WiFi LoRa 32 V3 microcontroller to sense the fields and a Raspberry Pi 4 to manage a web server and database. The hybrid rule-based scheduling algorithm was used to calculate feeding times depending on daily schedules and the threshold control algorithm was used to start the aerator when the dissolved oxygen dropped below 4.0 mg/L. The system was rated by 15 shrimp farmers with ZKD Farm in Kiamba, Sarangani Province on a 5-point Likert scale. The analysis resulted to an average weighted mean of 4.29/5.00, which is translated to Strongly Agree. The system developed is a fully automated, efficient, and reliable system to solve the problem of shrimp farming; it lowers the number of individuals to work on the farm, feed efficiency, and maximizes water quality and oxygen concentration. The technology will help in the sustainable production of aquaculture by reducing operational costs and enhancing the survival of shrimps.
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References
Abrajano, J. V., Apanay, J., Botangen, K. A., Nabua, J., & Peña, C. F. (2024). IoT-based water quality monitoring system for off-grid communities in the Philippines. Journal of Information Technology and Communication, 6(3), 82–91. ArXiv. https://arxiv.org/pdf/2410.14619
Balaji, K., et al., (2025). Low-cost IoT fish farming management system powered by Arduino and GSM. International Journal of Advanced Research in Computer and Communication Engineering, 14(5), 123–128.
Blancaflor, J., & Baccay, M. (2022). IoT-biofloc water quality management system for shrimp farming. International Journal of Advanced Computer Science and Applications, 13(4), 259–274. https://doi.org/10.1080/00051144.2022.2031540
Borbon, R., & Lumauag, E. (2024). Data-driven aquaculture monitoring system for prawn farms. Journal of Information Technology and Communication, 6(3), 75–90.
Calajate, A., et al., (2024). Arduino multi-sensor aquaculture water quality kit in Camarines Norte. Philippine Journal of Fisheries and Aquatic Resources, 12(2), 45–58.
Chen, Y., et al., (2022). IoT-enabled fish farm monitoring system with sensors for pH, DO, temperature, and water level. Computers and Electronics in Agriculture, 198, 107–115.
Dake, A., et al., (2024). IoT-based fish feeding and water monitoring with ESP32. International Journal of Innovative Research in Engineering and Science, 14(5), 14–19.
El Shal, H., et al., (2021). Design and fabrication of an automatic fish feeder prototype for tilapia tanks. Engineering, Technology and Applied Science Research, 11(3), 7140-7145.
Francisco, L., et al., (2024). Automatic fish feeder using Arduino Uno. International Journal of Research Publications, 248(032), 35–42.
Hossam, M., et al., (2025). Precision aquaculture: An integrated computer vision and IoT approach for optimized tilapia feeding. Computers and Electronics in Agriculture, 210, 668–675. https://openreview.net/forum?id=0IbIdDNYiR
Hridoy, R., et al., (2025). Predictive modeling of aquaculture water quality with IoT. Environmental Research, 221, 114–120.
Kanwal, S., et al., (2024). Machine learning to forecast survival rates using IoT-based decision-making system. Sensors, 24(23), 7842. https://doi.org/10.3390/s24237842
Khatri, P., et al., (2024). IoT-based aquaculture monitoring system with edge computing and AI. AgriEngineering, 7(2), 78.
Lin, J., et al., (2021). Wireless multi-sensor IoT system for aquaculture. Sensors, 21(8), 179.
Lindholm-Lehto, P. (2023). Water quality monitoring in recirculating aquaculture systems. Aquaculture, Fish and Fisheries, 3(2), 113–118.
Medina, J., et al., (2022). Open-source fish farming water quality monitoring buoy using LoRaWAN. Sensors, 22(15), 5678.
Mohamed, A., et al., (2024). IoT-based automatic fish feeding system with Firebase integration. International Journal of Advanced Research in Engineering and Technology, 15(3), 1–10.
Morial, R. (2024). Automated fish feeder using scheduler mobile application. Asian Journal of Research and Review in Agriculture, 12(2), 1–16.
Nayoun, K., et al., (2024). IoT precision fish farming using NodeMCU ESP8266 and Arduino IoT cloud. International Journal of Smart Electronics and Communication Systems, 13(2), 45–52.
Olanubi, A., et al., (2024). ESP32, GSM, and WhatsApp notification-based IoT water quality management system. Journal of Electrical Systems and Information Technology, 11(15), 1–15.
Papolonias, R., et al., (2025). Multi-sensor aquaculture water quality monitoring system. Mindanao State University. https://doi.org/10.13140/RG.2.2.39435.41942
Rivera, A., et al., (2021). IoT-based aquaculture control platform for tilapia ponds. IEEE Access, 9, 45210-45222.
Salazar, J., et al., (2025). MSU-Marawi real-time pond aeration and feeding prototype. Mindanao Journal of Science and Technology, 23(1), 78–92.
Santos, L. Q., et al., (2021). Automated aquaponics water monitoring system. Journal of Computing and Information Engineering Applications, 5(2), 120–126.
Shete, S., et al., (2024). IoT-enabled real-time water quality monitoring for tilapia ponds. Environmental Technology and Innovation, 33, 102583.
Sohail, M., et al., (2023). GSM-based smart aquarium monitoring and feeding system. International Journal of Smart Electronics and Communication Systems, 12(3), 1–6.
Teixeira, R., et al., (2021). IoT framework for precision aquaculture with low-cost communication and sensors. IEEE Internet of Things Journal, 8(12), 9876-9888.
Tolentino, K., et al., (2021). IoT-based modular aquaculture monitoring device via LoRaWAN. International Journal of Computing and Digital Systems, 10(5), 533–541.
Valencia, J., et al., (2022). Solar-powered fishpond feeder with real-time water quality monitoring. International Journal of Innovative Science and Research Technology, 7(4), 380–384.
Valerio, P., et al., (2023). Low-cost pond aerator controller in Laguna. Philippine Journal of Agricultural and Biosystems Engineering, 19(1), 45-53.
Vicencio, V., et al., (2025). Assessment of fishpond water quality in Bulacan. International Journal of Environmental Education and Development, 3(1), 25–34.
Villamor, E., et al., (2024). Multi-functional pond management system in Iloilo. Philippine Engineering Journal, 45(2), 112–125.
Wibisono, A., & Jayadi, R. (2024). IoT system for catfish ponds based on NodeMCU. International Journal of Advanced Computer Science and Applications, 15(3), 420–423.
Ylaya, G., et al., (2023). Automated fish feeder with mobile scheduler application for milkfish farming. Asian Journal of Agricultural Research, 11(2), 1–10.
