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Wavelength Selection for Classifying Paddy Rice by Near-Infrared Spectroscopy

¹PhD candidate, professor, and former research assistant, respectively. Dept. of Bio-Industrial Mechatronics Engineering, National Taiwan University. ²Corresponding author. Phone and fax: 886-2-33665329. E-mail: m320@ntu.edu.tw ³Agronomist, Taichung District Agricultural Improvement Station. ⁴Professor, Dept. of Agronomy, National Taiwan University.

Using five paddy rice cultivars grown in Central, Eastern, and Southern Taiwan and harvested in the summers of 1997, 1998, and 1999, eight calibrated models were established by discriminant analysis and back-propagation neural network with four wavelength selection methods. Randomly adding 80 samples of the 2000 year crop in the three-crop-year calibrated models for annual recalibration, eight models were used to classify paddy rice harvested in the summer of 2000. With 351 wavelengths of models 1 and 2, the average classification rates by discriminant analysis and backpropagation neural network were 98.1 and 92.5%, respectively. With 69 wavelengths selected by stepwise discrimination of models 3 and 4, the average classification rates by discriminant analysis and backpropagation neural network were 98.5 and 85.5%, respectively. With 69 wavelengths selected by correlation matrix of models 5 and 6, the average classification rates by discriminant analysis and neural network were 72.0 and 72.2%, respectively. With 69 wavelengths from loading values in the first and second principal components of models 7 and 8, the average classification rates by discriminant analysis and neural network were 69.1 and 60.6%, respectively. Model 3 would be recommended for classifying paddy rice to set trading prices because of its highest classification rate (98.5%).