Article Open Access

Improving TDS Sensor Accuracy in an IoT-Based Fertigation Prototype Using Polynomial Regression Calibration and Interpolation

Arif Harjanto, Happy Nugroho, Aprilia Amrina Ainurrosyidah, Aji Ery Burhandenny

Abstract


Accurate measurement of Total Dissolved Solids (TDS) is a critical requirement for hydroponic nutrient management, especially in automated fertigation systems that adjust nutrient concentrations based on plant age. However, non-industrial TDS sensors often exhibit significant fluctuations and non-linear errors, leading to unreliable nutrient dosing. This study proposes a calibration approach using second-order polynomial regression combined with interpolation to improve the accuracy of TDS measurements in an IoT-based fertigation prototype for lettuce hydroponics. The calibration was performed using reference TDS solutions and a digital TDS meter to ensure accurate measurements. The results show that the Mean Absolute Percentage Error (MAPE) decreased from 24.771% to 7.5768% during calibration, demonstrating a significant improvement in measurement accuracy. The calibrated sensor readings fall within the acceptable range for hydroponic nutrient control (±10%). This method provides a low-cost, reliable alternative for improving sensor accuracy in IoT fertigation systems.


Keywords


ESP32, Fertigation, IoT, Polynomial Regression, TDS Sensor Calibration

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DOI: https://doi.org/10.52088/ijesty.v6i1.1776

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International Journal of Engineering, Science, and Information Technology (IJESTY) eISSN 2775-2674