Regional soil water content monitoring based on time-frequency spectrogram of low-frequency swept acoustic signal

Abstract

Acoustic waves offer a non-destructive, safe, and cost-effective means of monitoring the environment, with a potential application in soil water content monitoring. However, extracting soil water information from acoustic signals is still challenging. To tackle this issue, we have developed a low-frequency swept acoustic signal detection device and system. We conducted soil penetration testing using low-frequency swept acoustic signals. The swept-frequency acoustic signals passing through the soil were transformed into time–frequency spectrogram. Using the Swin-Transformer model, we established a regression model between the time–frequency spectrogram of the swept frequencies and the soil water content. Predictions were made both on a laboratory test dataset and through field trials using the calibrated model. The results indicate that the RMSE, MAE, and R2 values between the observed and the model's outputs of water content (%) for the test laboratory dataset are 0.191, 0.081, and 0.999, respectively, using the Swin-Transformer model. In the case of the field trials, the RMSE, MAE, and R2 values between the predicted and observed values are 6.715 %, 1.829 %, and 0.711, respectively. These studies demonstrate that this method is highly effective in predicting soil water content, with the best results achieved at a resolution of 20 PPI (Pixels Per Inch) and within the frequency range of 260–360 Hz. It provides an efficient approach for acoustic soil water content detection, effectively resolves the difficulty in building models caused by the single-parameter limitation in traditional acoustic model.