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Re-Calibration of Model-Based Capacitive Sensor for IoT Soil Moisture
Measurements
Article
· December 2023
DOI: 10.30871/jaic.v7i2.6809
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Iman Setiawan
Universitas Tadulako
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Journal of Applied Informatics and Computing (JAIC)
Vol.7, No.1, December 2023, pp. 150~155
e-ISSN: 2548-6861
150
http://jurnal.polibatam.ac.id/index.php/JAIC
Re-Calibration of Model-Based Capacitive Sensor for IoT Soil Moisture
Measurements
Iman Setiawan
1
*, Mohammad Dahlan Th. Musa
2
**, Saskia Amalia Putri
3
*
* Statistic Study Program, Tadulako University, Palu, Indonesia
** Geophysical Engineering, Tadulako University, Palu, Indonesia
npl.untad@gmail.com
1
,
ochad1969@gmail.com
2
,
saskia.amalia.putri0108@gmail.com
3
Article Info
ABSTRACT
Article history:
Received 2023-11-18
Revised 2023-11-25
Accepted 2023-11-27
Low-cost automatic irrigation systems require quality calibrated soil moisture
sensors. The sensor is an indirect method of soil moisture measurement. The sensor
works based on the change in the dielectric constant. So, it requires to be calibrated
in terms of the soil water content. Polynomial and linear models are frequently used
to calibrate soil moisture sensor data in the gravimetric test method. However,
computational effort is required. This study aims to obtain a sensor calibration
application that can provide the best model of the available models for model-based
capacitive soil moisture sensor. This research was conducted using primary data
from gravimetric test experiment on Internet of things (IoT) based soil moisture
sensor. Web-based re-calibration application produced best model based on adjusted
R Squared. Finally, model-based capacitive soil moisture sensor set up using best
model coefficient. The results show that the web-based re-calibration application
can provide the best model for model-based capacitive soil moisture sensor. Based
on gravimetric test experiments and web applications, the best model is a polynomial
regression model order 3 with 0.945 adjusted R Squared. The model predicted value
for soil moisture is in the range 0 – 1.2 for raw sensor data values of 100 – 530. When
the model coefficient configured in capacitive soil moisture sensor and Blynk
application, soil moisture measurement can be done via mobile phone in real time.
Keyword:
Calibration,
IoT,
Polynomial Regression,
Soil Moisture Sensor,
Regression.
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