A multistructure neural network (MSNN) was proposed and applied to classify five classes of rice grains. The MSNN model consisted of five parallel multilayer feedforward neural networks (MLNN). With two hidden layers MLNN was trained using morphological and color features of the rice grains extracted from their images as input. The average classification accuracy of MSNN was 92.66%, with an increase of over 504 percent points than that of MLNN; moreover the network training time for MSNN was shorter than that for MLNN.
LIU Yingying,DING Weimin*,SHEN Mingxia
. Grading Rice Grains Using a MultiStructure Neural Network Approach[J]. Chinese Journal OF Rice Science, 2009
, 23(4)
: 440
-442
.
DOI: 10.3969/j.issn.1001-7216.2009.04.17
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