Estimation of water content in corn leaves using hyperspectral data based on fractional order Savitzky-Golay derivation coupled with wavelength selection

Abstract

Water status is critical since it affects photosynthetic efficiency and limits crop yield. Thus it is essential to calculate the water content of crop fast and nondestructively. In this work, hyperspectral reflectance in 900–1700 nm was used to estimate water content of corn leaves. The fractional order Savitzky-Golay derivation (FOSGD) was used to pretreat the hyperspectral data. The result indicated that the best performance of partial least square (PLS) model was obtained when the fractional order equaled to 1 and 2. In order to make a PLS model simpler, three wavelength selection approaches, variable importance in the projection (VIP), competitive adaptive reweighted sampling (CARS), and random frog were performed to extract the sensitive wavelengths. The result demonstrated that the PLS model with CARS and random frog wavelength selection did exhibit the best performance and extracted 23 feature wavelengths, with the coefficient of determination (R2) up to 0.91 for calibration and 0.92 for validation. The root mean squared error (RMSE) decreased to 0.049 for calibration and 0.044 for validation. Meanwhile, 80 percent of the feature wavelengths extracted by these two methods were similar. The findings of the current study may provide an effective way to predict the water content of corn leaves using a combination of FOSGD-CARS-PLS or FOSGD-random frog-PLS.