Open AccessEvaluation of Color Models for Palm Oil Fresh Fruit Bunch Ripeness Classification
Author(s) -
Nurbaity Sabri,
Zarina Bibi İbrahim,
Dino Isa
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v11.i2.pp549-557
Subject(s) - ripeness , ycbcr , rgb color model , artificial intelligence , mathematics , hsl and hsv , palm oil , color space , support vector machine , computer science , computer vision , pattern recognition (psychology) , color image , image processing , food science , image (mathematics) , ripening , chemistry , virus , virology , biology
This paper investigates the application of eight color models for automatic palm oil Fresh Fruit Bunch (FFB) ripeness classification with multi-class Support Vector Machine (SVM). Ripeness classification is important during harvesting to ensure that they are harvested during the correct ripe stage for optimum oil production. Since color is a significant indicator for agriculturists to determine the ripeness of FFB, it is critical to determine the right color model. Eight color models have been investigated namely, HSV, I1I2I3, LAB, XYZ, YCbCr, YIQ, YUV and RGB. Color moments were extracted from each of these color models for the classification of four stages of FFB ripeness that are unripe, under-ripe, ripe and over-ripe. A database of five hundred images of palm oil FFB has been constructed and experiments showed that YCbCr and YUV outperform the other color models.