Automated Grading of Palm Oil Fresh Fruit Bunches (FFB) Using Neuro-fuzzy Technique

Authors:
Nursuriati Jamil;Azlinah Mohamed;Syazwani Abdullah
Affiliations:
-;-;-
Venue:
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Year:
2009

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Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision

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Abstract

Automated fruit grading in local fruit industries are gradually receiving attention as the use of technology in upgrading the quality of food products are now acknowledged. In this paper, outer surface colors of palm oil fresh fruit bunches (FFB) are analyzed to automatically grade the fruits into over ripe, ripe and unripe. We compared two methods of color grading: 1) using RGB digital numbers and 2) colors classifications trained using a supervised learning Hebb technique and graded using fuzzy logic. A total of 90 images are used as the training images and 45 images are tested in the grading process. Overall, automated grading using RGB digital numbers produced an average of 49% success rate, while the neuro-fuzzy approach achieved an accuracy level of 73.3%.