Integration of Regression Analysis and Spatial Interpolation in Multi-Sensor Soil Calibration Optimization Based on RS485 to Support Precision Agriculture - Priyonggo, Budi; Hafidz, Muhammad; Azadi, Athoillah; Wirawan, Adi; Nucholis, Jati; Muharfiza, Muharfiza; Jahari, Mahirah (Jurnal Keteknikan Pertanian Tropis dan Biosistem, 2026)
Technological developments provide significant opportunities to utilise measurement equipment to enhance the effectiveness and efficiency of agricultural production. Accurate soil content measurement is very important for evaluating soil quality, helping farmers understand soil conditions and take appropriate steps to improve or maintain it. Calibration is required to ensure that the measuring instruments used provide accurate readings. The objective of this research was to calibrate the soil comprehensive sensor type RS485. The parameters tested are WET sensor, pH sensor, and temperature sensor. Data analysis was conducted using spatial interpolation, linear regression, and multiple linear regression. The calibration results obtained the calibration function  (R2 0.9566) for the WET sensor, (R2 0.9845) for the pH sensor, and  (R2 0.9963) for T sensor. The results of the spatial interpolation analysis using the calibration function indicate an improvement in the accuracy between the tested sensor and the calibrator compared to before calibration. The improvement in the accuracy of the parameter values was obtained by performing multiple linear regression analysis involving all sensors. The equations WETe4, pHe4, and Te4 were chosen as the best estimated equations that can improve the accuracy of sensor readings. The results of spatial interpolation analysis of the functions WETe4, pHe4, and Te4 showed a better improvement in accuracy compared to the calibration function. The equations WETe4, pHe4, and Te4 can be used as alternative functions to approximate the correct values in the use of soil comprehensive sensor type RS485. The spatial interpolation analysis conducted can provide an illustration of values based on color distribution to understand of the distribution of those values.