Estimating Soil Moisture Content Using Hyperspectral Imaging

－ Moisture content estimation model using XGBoost and Bayesian optimization－

Abstract

Hyperspectral cameras are VNIR sensors that can detect the electromagnetic spectrum incident from an object with high wavelength resolution and have recently been expected to be applied in various fields. In this study, we applied hyperspectral cameras to the construction field and investigated the possibility of extracting areas of interest and estimating the approximate amount of moisture contained in the embankment. Hyperspectral data of fill under construction taken with the cooperation of actual field workers were used for the data. For the extraction of regions of interest, we confirmed that if the average teacher spectrum of the target region could be prepared, it would be possible to extract regions that are like the spectral waveform. Next, regarding the estimation of moisture content, we created a prediction model consisting of ensemble learning such as XGBoost using teacher data labeled by moisture content for the extracted fill and obtained a classification accuracy of 94%. These results suggest that hyperspectral cameras have potential applications in the construction field.