Rapid determination of water content in potato tubers based on hyperspectral images and machine learning algorithms

This study investigated the hyperspectral reflectance response of time series generated during oven drying to changes in the moisture content of potato tubers. Seventeen preprocessing methods were used to eliminate the influence of spectral noise on the spectral characteristic curve. Algorithms such as CatBoost, LightGBM, and XGBoost are used to obtain the first 40 effective characteristic spectra of hyperspectral images, which reduces the redundancy of data and improves the prediction accuracy. The water content prediction model of potato tubers was established by using the selected characteristic bands. The results showed that the combined model based on Lasso and XGBoost algorithm had the strongest prediction ability. The best model is MF-Lasso-XGBoost, which has R-2 value of 0.8908, Rmse of 0.0610, Mdae of 0.0389, and R-cv(2) of 0.8448. This research can provide reference for the detection of potato moisture content and theoretical basis for the development of crop moisture detector.