THE EFFECT OF WAVELENGTH SELECTION OF NEAR INFRARED SPECTRA ON CLASSIFYING PADDY RICE

摘要

Five varieties of paddy rice were examined using the reflectance spectra corresponding to a selected wavelength from 1100 to 2500 nm in 3-nm steps to determine the classification rate effect. Three hundred fifty-one variables were used to develop the discriminant analysis and neural network models. The average classification rates were 99.69% and 97.69%, respectively. Sixty-two variables were selected using stepwise discrimination to develop the discriminant analysis and neural network models. The average classification rates were 98.0% and 92.76%, respectively. Sixty-two variables were selected using the correlation matrix to develop the discriminant analysis and neural network models. The average classification rates were 90.15% and 84.26%, respectively. Sixty-two variables were selected by loading the first and second principal components to develop the discriminant analysis and neural network models. The average classification rates were 89.38% and 82.25%, respectively. The stepwise discrimination method was more effective in classifying the five varieties of paddy rice using near infrared spectra.