Pomelo’s Quality Classification Based on Combination of Color Information and Gabor Filter

  • Huu-Hung HuynhEmail author
  • Trong-Nguyen Nguyen
  • Jean Meunier
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 244)


Vietnam is a country with strength in fruit trees, including many fruits well-known to the world, such as pomelo, dragon fruit, star apple, mango, durian, rambutan, longan, litchi and watermelon. However, the competitiveness and export of these fruits are low and incommensurate with the existing potential. To solve this problem, Vietnam is studying sustainable directions by investing in machinery for automation process to meet international standards. In this paper, we introduce an effective method for detecting surface defects of the pomelo automatically based on the combination of color information and Gabor filter. Our approach begins by representing the input image in HSV color space, computing the compactness based on the H channel, extracting texture parameters and using the K-nearest neighbor algorithm for quality classification. The proposed approach has been tested with high accuracy and is promising.


Input Image Color Space Surface Defect Gabor Filter Gabor Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    Dacal-Nieto, A., Vzquez-Fernndez, E., Formella, A., Martin, F., Torres-Guijarro, S., Gonzlez-Jorge, H.: A genetic algorithm approach for feature selection in potatoes classification by computer vision. In: IECON 2009. IEEE, Portugal (2009)Google Scholar
  3. 3.
    Deng, Y., Qin, S., Wu, Y.: An Automatic Food Recognition Algorithm with both Shape and Texture Information. In: PIAGENG 2009. SPIE, China (2009)Google Scholar
  4. 4.
    Hetal, N., Patel, R.K., Jain, M.V.: Fruit Detection using Improved Multiple Features based Algorithm. IJCA 13(2) (January 2011)Google Scholar
  5. 5.
    Panli, H.E.: Fruit Surface Defects Detection and Classification Based on Attention Model. JCIS 2012 8(10) (2012)Google Scholar
  6. 6.
    Srivastava, D.K.: Efficient fruit defect detection and glare removal algorithm by anisotropic diffusion and 2d gabor filter. IJESAT 2(2), 352–357 (2012)Google Scholar
  7. 7.
    Kim, D.G., Burks, T.F., Qin, J., Bulanon, D.M.: Classification of grapefruit peel diseases using color texture feature analysis. IJABE 2(3) (September 2009)Google Scholar
  8. 8.
    Alom, M.Z., Lee, H.J.: Gaussian Mean Based Paddy Disease Segmentation. In: ICMV 2010, pp. 522–525 (2010)Google Scholar
  9. 9.
    Sun, D.-W.: Computer vision technology for Food Quality Evalution. Elsevier (2008)Google Scholar
  10. 10.
    Seo, N.: Texture Segmentation using Gabor Filters. In: ENEE 731 Project (November 8, 2006)Google Scholar
  11. 11.
    Jha, S.N.: Nondestructive evaluation of Food Quality Theory and Practice. Springer (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Huu-Hung Huynh
    • 1
    Email author
  • Trong-Nguyen Nguyen
    • 1
  • Jean Meunier
    • 2
  1. 1.DATIC, Department of Computer ScienceUniversity of Science and TechnologyDanangVietnam
  2. 2.DIROUniversity of MontrealMontrealCanada

Personalised recommendations