THE EFFECT OF WAVELENGTH SELECTION OF NEAR INFRARED SPECTRA ON CLASSIFYING PADDY RICE
Date Issued
2005-06
Date
2005-06
Author(s)
Liu, Chang-Chun
Shaw, Jai-Tsung
Poong, Keen-Yik
Hong, Mei-Chu
Shen, Ming-Lai
DOI
20060927123002273144
Abstract
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.
Subjects
Near infrared
Artificial neural network
Paddy rice
Variable selection
Classification
Publisher
臺北市:國立臺灣大學生物產業機電工程學系
Type
journal article
File(s)![Thumbnail Image]()
Loading...
Name
14-2-3.pdf
Size
1.87 MB
Format
Adobe PDF
Checksum
(MD5):e27d7d3d9124dcf1fc376da77b0f7834