Fruit Recognition Using Surface and Geometric Information

Authors

  • Joel C. De Goma School of Information Technology, Mapúa University, Makati 333 Sen. Gil Puyat Ave., Makati City 1200, Philippines.
  • Cyrus Arlan M. Quilas School of Information Technology, Mapúa University, Makati 333 Sen. Gil Puyat Ave., Makati City 1200, Philippines.
  • Maurice Albert B. Valerio School of Information Technology, Mapúa University, Makati 333 Sen. Gil Puyat Ave., Makati City 1200, Philippines.
  • Jon Jake P. Young School of Information Technology, Mapúa University, Makati 333 Sen. Gil Puyat Ave., Makati City 1200, Philippines.
  • Zaliman Sauli School of Microelectronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.

Keywords:

Fruit Recognition, Co-Occurrence Features, Pre-Processing, Feature Extraction, K- Nearest Neighbor,

Abstract

One of the interesting topics in image processing and computer vision is Fruit Recognition. The computer vision strategies used to recognise fruits rely on four basic features which are colour, texture, size and shape. In fruit recognition, unrecognised fruit images are caused by different factors. These factors are different illuminations, specular reflections, and different poses of each fruit, variability on the number of elements, and cropping or occlusions. This paper proposes and aims an efficient and effective way to recognise fruits regardless of the said factors by combining the four basic features of the fruit. Fruit recognition involves different processes which are pre-processing, feature extraction, recognition and testing. The recognition is done using the K-Nearest Neighbor based on statistical values of the colour moments, Gray Level Cooccurrence Matrix (GLCM) features, area by pixels for the size and shape roundness. The fruit images comprised of 2633 fruit images from 15 different kinds of fruits. The authors tested different classifiers which are KNN, Naïve Bayes, Decision Tree, and bagging to know what best fits for the images. After testing the classifiers based on the 2633 images, results showed that KNN outperformed the other classifiers. The result showed that combining all the features namely colour, texture, size and shape, the overall recognition rate for all classifiers has increased and it has shown the best output.

References

Gonzalez, R., Woods, R. 2008. “Digital Image Processing Third Edition”. Pearson Prentice Hall

Ibrahim, A., Eissa, A., Alghannam, A. “Image Processing System for Automated Classification Date Fruit”, International Journal of Advanced Research (2014), vol. 2, issue 1, pp.702-715, 2014.

Arivazhagan, S., Shebiah R., Nidhyanandhan S., Ganesan L. “Fruit Recognition using Color and Texture Features”, Journal of Emerging Trends in Computing and Information Sciences, vol. 1, no. 2, pp.90-94, 2010.

Holalad, H., Warrier, P., and Sabarad, A. “An FPGA based Efficient Fruit Recognition System Using Minimum Distance Classifier”, Journal of Information Engineering and Applications, vol. 2, no. 6, 2012.

Rocha, A., Hauagge, D., Wainer, J., and Goldenstein, S. “Automatic Produce Classification from Images using Color, Texture and Appearance Cues”, XXI Brazilian Symposium on Computer Graphics and Image Processing, pp.3-10, 2009.

Faria, F., Dos Santos, J., Rocha, A., and Torres, R. “Automatic Classifier Fusion for Produce Recognition”, Institute of Computing, University of Campinas, 2012.

Chaw, J. and Mokji, M. “Produce Recognition System Using Data Mining Algorithm”, 2012 IEEE International Conference on Control System, Computing and Engineering, pp.39-43, 2012.

F. Kiamarzpour, R. Dianat, and M. Sadeghzadeh, “Improving the methods of email classification based on words ontology,” arXiv Prepr. arXiv1310.5963, 2013.

Downloads

Published

2018-05-30

How to Cite

De Goma, J. C., Quilas, C. A. M., Valerio, M. A. B., Young, J. J. P., & Sauli, Z. (2018). Fruit Recognition Using Surface and Geometric Information. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-15), 39–42. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4043

Issue

Vol. 10 No. 1-15: Exploring Man-Machines Compliance in Robotics and Image Processing

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.


Most read articles by the same author(s)

1 2 > >>