Ground Penetrating Radar (GPR) Identification Method for Agricultural Soil Stratification in a Typical Mollisols Area of Northeast China.

In order to achieve a rapid and accurate identification of soil stratification information and accelerate the development of smart agriculture, this paper conducted soil stratification experiments on agricultural soils in the Mollisols area of Northeast China using Ground Penetrating Radar (GPR) and obtained different types of soil with frequencies of 500 MHz, 250 MHz, and 100 MHz antennas. The soil profile data were obtained for 500 MHz, 250 MHz, and 100 MHz antennas, and the dielectric properties of each type of soil were analyzed. In the image processing procedure, wavelet analysis was first used to decompose the pre-processed radar signal and reconstruct the high-frequency information to obtain the reconstructed signal containing the stratification information. Secondly, the reconstructed signal is taken as an envelope to enhance the stratification information. The Hilbert transform is applied to the envelope signal to find the time-domain variation of the instantaneous frequency and determine the time-domain location of the stratification. Finally, the dielectric constant of each soil horizon is used to obtain the propagation velocity of the electromagnetic wave at the corresponding position to obtain the stratification position of each soil horizon. The research results show that the 500 MHz radar antenna can accurately delineate Ap/Ah, horizon and the absolute accuracy of the stratification is within 5 cm. The effect on the soil stratification below the tillage horizon is not apparent, and the absolute accuracy of the 250 MHz and 100 MHz radar antennas on the stratification is within 9 cm. The overwhelming majority of the overall calculation errors are kept to within 15%. Based on the three central frequency antennas, the soil horizon detection rate reaches 93.3%, which can achieve accurate stratification of soil profiles within 1 m. The experimental and image processing methods used are practical and feasible; however, the GPR will show a missed detection for soil horizons with only slight differences in dielectric properties. Overall, this study can quickly and accurately determine the information of each soil stratification, ultimately providing technical support for acquiring soil configuration information and developing smart agriculture. [ABSTRACT FROM AUTHOR]