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Calibration and Assessment of Capacitance-Based Soil Moisture Sensors

机译:基于电容的土壤湿度传感器的校准和评估

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The aim of this paper was: (1) to establish soil-specific laboratory calibration equations for two types of volumetric water content sensors (5TE and GS3); and (2) to evaluate their measurement accuracy and precision for estimating the soil moisture contents in sand, based on the established laboratory calibration equations and the corresponding default factory calibration equations provided by the manufacturers. The study revealed that the developed laboratory calibration equations (linear and polynomial) improved the sensors' measurement accuracy compared to that obtained using their corresponding factory calibration equations. Based on the root mean square error (RMSE), the 5TE sensor exhibited higher accuracy (RMSE=1.15%) with the third-order polynomial equation, followed by the second-order equation (RMSE=1.32%) and a linear regression equation (RMSE=1.63%). Thus, the third-polynomial type equation was considered to be the most suitable calibration model for the 5TE sensors in sand. In contrast, the second-order polynomial calibration equation provided highest accuracy for the GS3 sensors with the lowest RMSE of 0.86%.
机译:本文的目的是:(1)为两种类型的体积水含量传感器(5TE和GS3)建立针对土壤的实验室校准方程式; (2)根据已建立的实验室校准方程式和制造商提供的相应默认工厂校准方程式,评估其测量精度和精度,以估算沙子中的土壤水分含量。研究表明,与使用相应的工厂校准方程式相比,已开发的实验室校准方程式(线性和多项式)提高了传感器的测量精度。基于均方根误差(RMSE),5TE传感器具有更高的精度(RMSE = 1.15%),具有三阶多项式方程式,其次是二阶方程式(RMSE = 1.32%)和线性回归方程式( RMSE = 1.63%)。因此,第三多项式方程被认为是最适合砂岩中5TE传感器的校准模型。相反,二阶多项式校准方程为GS3传感器提供了最高的精度,最低的RMSE为0.86%。

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