THE EFFECT OF PARAMETER SELECTION ON CLASSIFYING PADDY RICE
Date Issued
2005-03
Date
2005-03
Author(s)
Liu, Chang-Chun
Shaw, Jai-Tsung
Poong, Keen-Yik
Hong,
Mei-Chu
Shen Ming-Lai
DOI
20060927122950210444
Abstract
Considering different shape factors and color intensities of five varieties of paddy
rice as parameters, four models were established by a back-propagation neural
network and were used to study the validation and classification rates affected by
choosing different parameters. With 60 parameters, the average validation and
classification rates were 92.24% and 92.0% respectively. If the most effective 50 parameters were chosen by loading values in the first principal component, the
average validation and classification rates were 91.77 % and 90.0% respectively. 30
parameters selected from the correlation coefficient matrix to build up the model,
the average validation and classification rates were 89.18 % and 91.4% respectively.
If the most effective 20 parameters were chosen from model training, the average
validation and classification rates were 90.59 % and 91.8% respectively, which could
be the best model for classifying due to its less parameters and better stability.
Subjects
Machine vision
Artificial neural network
Paddy rice
Parameter 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
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