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

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%.

[1]  Musa Mohd Mokji,et al.  Starfruit classification based on linear hue computation , 2007 .

[2]  Meftah Salem M. Alfatni,et al.  Oil palm fruit bunch grading system using red, green and blue digital number. , 2008 .

[3]  Aytürk Keles,et al.  Neuro-fuzzy classification of prostate cancer using NEFCLASS-J , 2007, Comput. Biol. Medicine.

[4]  Rizauddin Ramli,et al.  Development of Jatropha Curcas color grading system for ripeness evaluation , 2009 .

[5]  Mohd Halim Shah Ismail,et al.  Digital Image Processing of Palm Oil Fruits , 2006 .

[6]  Yasuo Saito,et al.  Neural Network Application to Eggplant Classification , 2003, KES.

[7]  C. C. Teoh,et al.  USE OF IMAGE ANALYSIS FOR GRADING SIZE OF MANGO , 2006 .

[8]  Wan Ishak WanIsmail,et al.  Optical properties for mechanical harvesting of oil palm FFB. , 2000 .

[9]  N. Kondo,et al.  Automated fruit grading system using image processing , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[10]  L. C. Guan,et al.  The applications of computer vision system and tomographic radar imaging for assessing physical properties of food , 2004 .

[11]  M. Z. Abdullah,et al.  Stepwise Discriminant Analysis for Colour Grading of Oil Palm Using Machine Vision System , 2001 .

[12]  Abdelhamid Abdesselam,et al.  Pepper berries grading using artificial neural networks , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).

[13]  Ahmad Ihsan Mohd Yassin,et al.  Non-destructive watermelon ripeness determination using image processing and artificial neural network (ANN) , 2009 .

[14]  Siva Kumar Balasundram,et al.  Relationship between oil content and fruit surface color in oil palm (Elaeis guineensis Jacq.). , 2006 .

[15]  A. S. Fathinul-Syahir,et al.  Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system , 2006 .

[16]  Clifford J Studman,et al.  Computers and electronics in postharvest technology : a review , 2001 .