Calibration and performance evaluation of Internet of Things-based soil moisture sensors under saline irrigation conditions
2025
Najme Yazdanpanah | Younsuk Dong
Internet of things technologies have enabled real time monitoring soil moisture through sensor networks. However, the accuracy of this system depends on proper calibration under different soil conditions. This research is presented to quantify the effect of salinity on the accuracy of five different soil moisture sensors, including Soil Watch 10, GroPoint, CS655, Drill & Drop and TDR 310H The laboratory experiment was conducted using a soil column which irrigated with different levels of salinity (EC= 0, 2, 4, 6, 8 dSm−1). For more accuracy, proven method gravimetric was adopted for calibration reference during soil moisture measurement. Regression equations, along with gravimetric data, are used for the best fitting. The results showed that the overestimation and underestimation degrees of all sensors increased with increasing salinity levels. Only TDR did not exhibit distortion at high-salinity levels. Both linear and nonlinear (power and exponential) calibration models were assessed for their ability to describe the relationship between sensor outputs and gravimetric reference moisture data under a range of EC levels. Under low-salinity conditions (EC < 2 dS m⁻¹), a strong linear correlation (R² > 0.91) was observed for most sensors, excluding TDR. The exponential model consistently performed well with the desired accuracy output under saline conditions. TDR is suggested as the best choice for high soil salinity levels.
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