GRADING OF YEMENI RAISIN (RAZIGI CV.) Using Non-Contact Machine Vision Techniques
El-Faki, M., Al-Hazami, M. (2009). GRADING OF YEMENI RAISIN (RAZIGI CV.) Using Non-Contact Machine Vision Techniques. EKB Journal Management System, 23(1), 86-93. doi: 10.21608/fjard.2009.197039
M. S. El-Faki; M. H. Al-Hazami. "GRADING OF YEMENI RAISIN (RAZIGI CV.) Using Non-Contact Machine Vision Techniques". EKB Journal Management System, 23, 1, 2009, 86-93. doi: 10.21608/fjard.2009.197039
El-Faki, M., Al-Hazami, M. (2009). 'GRADING OF YEMENI RAISIN (RAZIGI CV.) Using Non-Contact Machine Vision Techniques', EKB Journal Management System, 23(1), pp. 86-93. doi: 10.21608/fjard.2009.197039
El-Faki, M., Al-Hazami, M. GRADING OF YEMENI RAISIN (RAZIGI CV.) Using Non-Contact Machine Vision Techniques. EKB Journal Management System, 2009; 23(1): 86-93. doi: 10.21608/fjard.2009.197039

GRADING OF YEMENI RAISIN (RAZIGI CV.) Using Non-Contact Machine Vision Techniques

Fayoum Journal of Agricultural Research and Development
Article 28, Volume 23, Issue 1, January 2009, Page 86-93  XML PDF (360.6 K)
Document Type: Research articles.
DOI: 10.21608/fjard.2009.197039
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Authors
M. S. El-Faki1; M. H. Al-Hazami2
1Agricultural Engineering Department, Faculty of Agriculture, Sana'a University
2Horticulture Department, Faculty of Agriculture, Sana'a University Republic of Yemen
Abstract
This research aimed at establishing quality standards for noncontact grading of Yemeni raisin (Vitis vinifera) of Razigi variety, by
developing a digital image processing algorithm based on color and
shape information. This was accomplished through developing preprocessing procedures to segment transparent interior areas in raisin
color images as the regions of interest and highlight their morphology
for extracting shape features. A distinct signature for each raisin grade
was generated by calculating number of matches of a set of twenty
selected morphological features. A minimum distance classifier was
developed, trained and tested in grading raisins by sorting them into
three grades, namely, A, B, and C. The classifier was successful in
sorting two raisin grades, namely, A and C with 100% CCR and 0%
MCR for each. Its performance was not good enough in sorting raisins
of grade B as the CCR dropped to 50%. Some difficulties were
encountered by the classifier in sorting half of the raisins of grade B
due to their great similarity with grade A raisins. The developed
algorithm confirmed its perfect performance in distinguishing between
grades A and C, and successfully initiated quality standards for grading
raisins using non-contact machine vision techniques with the need for
some improvement to extend its precision to encompass grade B.

Keywords
Raisin; Quality standards; Non-contact grading; Machine vision; Color information; Transparent areas; Region structural shape
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