Affiliation:
1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
2. China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
Abstract
Soil water sensors based on the standing wave rate (SWR) principle are affected by temperature in long-term operation. To address this problem, a temperature compensation model based on the binary regression analysis method is proposed. The measurement results of the temperature-compensated standing wave rate (TCSWR) sensor at different temperatures and soil volumetric water content are analyzed, and the least-squares principle is used to identify the parameters to be determined in the compensation model for temperature for the SWR soil water sensor. A portable tapered TCSWR sensor with built-in temperature compensation model was developed on this basis. The calibration results show that the standing wave measurement circuit of the TCSWR sensor can effectively respond to changes in soil water, and the coefficient of the fitted equation exceeds 0.95. A comparison of the results before and after temperature compensation proves that compensation can significantly reduce the measurement error of the TCSWR sensor and improve the measurement accuracy. The static and dynamic characteristics of the TCSWR sensor show that the measurement range of the TCSWR sensor is7.50%-31.50%, the measurement accuracy is ±0.63%, the stability is good, the resolution is a minimum of 0.05%, and the dynamic response time is less than 1 s. The absolute error of the TCSWR sensor measurement is less than 1% in comparison with similar sensors, demonstrating that the measurement results of the TCSWR sensor are reliable.
Funder
Beijing Technology and Business University
Subject
Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering