| Summary: | 碩士 === 國立中興大學 === 農業機械工程學系 === 84 === The purpose of this thesis is to develop digital image
processingtechniques to extract feature parameters of cut roses,
and to use neuralnetwork to simulate the manual grading
experiences for cut roses grading. Two color images were
grabbed for each rose, one of which was thewhole cut rose image
for analyzing the morphological features of the stem,the other
was the bud image for analyzing the bud features. The
stemsegmentation method was first to define the stem image
characteristics,then to search the image column by column based
on the characteristicsdefined, and finally to label the stem
segments. To segment the bud image,the color segmentation and
the dilation and erosion techniques were utilizedand the color
information of the bud was not changed. Ten feature
parameterswere extracted for each cut rose. The stem
straightness parameters were themaximum crooked angle, the
maximum deviated distance, and the average deviateddistance. The
stem diameter parameters were the bottom diameter, the
middlediameter, and the top diameter. And the bud maturity
parameters were theprojected area, the perimeter, the
compactness, and the principal axes. Partof the 10 features were
selected and inputted to an error back-propagationneural network
to simulate human quality grading operations for cut roses.The
length grading was run only by the image processing program.
The cut roses length grading accuracy is 93%, and the
identificationrate with the best neural network model obtained
in this study is 70.7%,compared with human grading results.
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