A method of electrical conductivity compensation in a low-cost soil moisture sensing measurement based on capacitance. (January 2020)

• Record Type: Journal Article
• Authors: Deng, Xiao; Gu, Huinan; Yang, Lei; Lyu, Huafang; Cheng, Yuanguo; Pan, Lipeng; Fu, Zijian; Cui, Liqin; Zhang, Li
• Is Part Of: Measurement. Volume 150(2020)
• Publication Date: 2020-01
• Subjects: Soil moisture sensors -- Capacitance detection -- Conductivity detection -- Low-frequency -- Mathematical model; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107052
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Proposed a conductivity compensation method for detecting moisture at LF. Two kinds of probes for measuring capacitance and conductivity were designed. Designed a real-time monitoring system which can be used in field application. Establish the optimized mathematical model based on capacitance and conductivity. Abstract: The detection of soil moisture is one of the most important researches in the development of precision agriculture. At present, the detection of soil dielectric constant is the most effective and convenient method to measure soil moisture content. Most of the dielectric type soil moisture sensors reduce the influence of soil salinity, texture and other factors on the measurement by detecting at high frequency. However, this will increase the production cost of the measurement circuit and fail to meet the requirements for large-scale deployment of sensors in agricultural production. The method of low-frequency capacitance detection has a price advantage but a low measurement accuracy, thus the application is very limited. This paper intends to improve the measurement accuracy and application range of low-frequency capacitance moisture sensors by correcting the relationship between low-frequency capacitance and moisture through the detection of soil conductivity. Three modified models (logistic, exponential and polynomial) to estimate the moisture content was verified by comparing with the oven-drying method calibration value. The results indicated that the MAME and MAE of the logistic model was less than 3.55% and 2.50%, respectively, which can satisfy most agricultural production requirements for soil moisture detection. Therefore, the study provided an effective detection method for low-cost soil moisture detection in low salinity and low organic matter soils.