<![CDATA[Real-time water quality monitoring is crucial for modern aquaculture. However, low-cost Internet of Things (IoT) systems frequently struggle with analog sensor precision due to the limitations of internal microcontrollers. This study presents the design, calibration, and performance evaluation of a highly precise IoT-based water quality monitoring system. The hardware architecture utilizes NodeMCU ESP-32 microcontroller integrated with an external ADS1115 16-bit Analog-to-Digital Converter (ADC) module. This integration effectively mitigates signal noise and accurately processes analog inputs. The system continuously measures temperature using a DS18B20 sensor, alongside pH, Dissolved Oxygen (DO), and Total Dissolved Solids (TDS). To ensure industrial-grade reliability, rigorous sensor calibration was executed using linear regression and standard buffer solutions prior to deployment. A 14-day comparative field test was then conducted against calibrated commercial handheld instruments to validate the system's accuracy. The statistical evaluation demonstrated exceptional precision, yielding minimal average measurement errors of 0.08°C for temperature, 0.35 for pH, 0.24 mg/L for DO, and 6.80 ppm for TDS. Furthermore, linear regression analysis confirmed highly robust data correlations between the IoT sensors and the standard devices. The system achieved coefficient of determination ($R^2$) values of 0.9928 for the temperature sensor, 0.8906 for pH, 0.9962 for DO, and 0.7656 for TDS. These results mathematically confirm that integrating an external high-resolution ADC alongside comprehensive statistical calibration significantly enhances measurement stability. Ultimately, this approach successfully elevates the precision of low-cost IoT monitoring systems for aquaculture applications.]]>
