Statistical Models for Prediction of Dry Weight and Nitrogen Accumulation Based on Visible and Near-Infrared Hyper-Spectral Reflectance of Rice Canopies

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Much information is obtainable from hyper-spectral data, which measure solar radiation consecutively at less than about 10-nm intervals.In constructing statistical prediction models, however, problems pf overfitting may arise due to the excessive number of variables, and multicollinearity may occur between variables ; thus a few specific wavelengths must be chosen.Various multivariate regression models were examined with ten-fold cross-validation to develop effecient, accurate models to predict dry weight and nitrogen accumulation of rice crops from the maximum tiller number stage to the meiosis stage, using plant-canopy reflectance of hyper-spectra within the 400-1100 nm domain without any variable selection.The results showed that the principal component regression using hyper-spectra gave better fits and predictability than that using specific wavelengths.On the other hand, partial least squares regression was the most useful among the models tested ; this method avoided overfitting and multicollinearity by using all wavelength information without variable selection and by inclusion of both x and y variations in its latent variables.
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