Agarwood chip grading based on color using image processing and artificialintelligence methods
Agarwood is the primary material especially in perfume industry. If agarwood with different grades are mixed, the quality will decrease and hence the products prices and quality will decrease. During the grading stages, human knowledge and experience is critically used in decision making. However...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/98038/1/FK%202015%20194%20UPMIR.pdf http://psasir.upm.edu.my/id/eprint/98038/ |
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| Institution: | Universiti Putra Malaysia |
| Language: | English |
| Summary: | Agarwood is the primary material especially in perfume industry. If agarwood
with different grades are mixed, the quality will decrease and hence the
products prices and quality will decrease. During the grading stages, human
knowledge and experience is critically used in decision making. However,
human characteristics often tender to fatigue where lead to produce
misclassification. The price of agarwood is influenced by the resin content
which can be indicated by the colour. The highest grade of agarwood chips has
a shining black colour while the lower grade of agarwood chips has a black
colour that is alternating with the brown colour. This study was conducted to
determine the relationship of agarwoodcolour properties and its related price by
adopting the method of artificial intelligence and image processing.
Colouragarwood images in Red, Green, Blue, (RGB), Hue, Saturation, Intensity
(HIS) and CIE colorimetric space (CIELAB) has been evaluated by comparing
the performance of colour pixels classification using Fuzzy C-Means (FCM)
method. The performance measurement was done through the evaluation of
classification accuracy using 5 cluster validity indices. The result of cluster
validity indices shows that CIELAB colorspace with 4 number of cluster proven
the most consistent in FCM classification. In the later stage, the use of
statistical measurement i.e. Analysis of Variance (ANOVA) and Duncan
Multiple Range Test (DMRT) gave a significant relationship when classifying
five out of seven grades of agarwood chips used i.e. RM250, RM350, RM800,
RM900 and RM2500. Then, the artificial intelligence system using fuzzy logic
and neural network concept has been developed and their performance has
been compared. The result shows that fuzzy logic system successfully
classified 62.8% of overall accuracy while neural network system gives 58% of
overall accuracy in grading the agarwood chips. As a conclusion, the proposed
system is helpful to the agarwood industry especially in determination of
agarwood chips color during the grading process. |
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