Development of water quality monitoring system for fish farming - B. Papolonias, Juffil; Q. Lavilles, Rabby; I. Miano, Joel (Institute of Advanced Engineering and Science, 2025)
Tilapia fish farming faces growing challenges from climate variability, environmental degradation, and the urgent demand for sustainable food production. However, traditional water quality monitoring methods remain manual and reactive, often resulting in compromised fish health and reduced farm productivity. Addressing this need, this study designed and developed a water quality monitoring system utilizing the internet of things (IoT) and embedded systems to enable real-time, proactive management. Guided by the software development life cycle (SDLC), the methodology focused on planning and analysis, system design and development, and testing and evaluation. The system integrates key water quality sensors, including pH, temperature, dissolved oxygen (DO), and electrical conductivity (EC), identified as critical parameters affecting tilapia health. These sensors were interfaced with Arduino Nano and ESP32 Dev Kit microcontrollers, forming the sensing layer of the system. Sensor data were transmitted to the ThingSpeak IoT platform for real-time visualization and storage. Validation results revealed a low mean absolute percentage error (MAPE), indicating an acceptable sensor performance. User evaluation, based on the technology acceptance model (TAM), indicated that the system was perceived as useful, user-friendly, and valuable for aquaculture management. Overall, the system enables real-time water quality monitoring, supporting a more responsive and sustainable environment for tilapia fish farming.