Image Analysis for Efficient Surface Defect Detection of Orange Fruits

  • Thendral Ravi
  • Suhasini Ambalavanan
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


This work portrays a novel approach for the improvement of a real-time computerized vision based model for automatic orange fruit peel defect detection. In this paper at first, different filtering methods and wavelet based method has been used to denoise the given input image and performs their comparative study. Based on this study, the wavelet based approach is used for smoothening of the images together with removing the higher energy regions in an image for better defect detection as well as makes the defects more retrievable. Finally, orange fruit skin color defects are identified by using RGB and HSI color spaces. The experimental test results indicate that the designed algorithm is scalable, computationally effective and robust for identification of orange fruit surface defects.


Image processing Color spaces Machine vision Background segmentation Defect detection 


  1. 1.
    Diaz, R., Gil, L., Serrano, C., Blasco, M., Molto, E., Blasco, J.: Comparison of three algorithms in the classification of table olives by means of computer vision. J. Food Eng. 61(1), 101–107 (2004)CrossRefGoogle Scholar
  2. 2.
    Miller, B.K., Delwiche, M.J.: Peach defect detection with machine vision. Trans. ASAE 34(6), 2588–2597 (1991)CrossRefGoogle Scholar
  3. 3.
    Cerruto, E., Failla, S., Schillaci, G.: Identification of blemishes on oranges. In: International Conference on Agricultural Engineering, AgEng 96, Madrid, EurAgEng Paper No. 96F–017 (1996)Google Scholar
  4. 4.
    Muir, A.Y., Porteous, R.L., Wastie, R.L.: Experiments in the detection of incipient diseases in potato tubers by optical methods. J. Agr. Eng. Res. 27(2), 131–138 (1982)CrossRefGoogle Scholar
  5. 5.
    Shearer, S.A., Payne, F.A.: Color and defect sorting of bell peppers using machine vision. Trans. ASAE 33(6), 1245–1250 (1990)CrossRefGoogle Scholar
  6. 6.
    Singh, N., Delwiche, M.J.: Machine vision methods for defect sorting stonefruit. Trans. ASAE 37(6), 1989–1997 (1994)CrossRefGoogle Scholar
  7. 7.
    Pearson, T.: Machine vision system for automated detection of stained pistachio nuts. LWT—Food Sci. Tech. 29(3), 203–209 (1996)Google Scholar
  8. 8.
    Wulfsohn, D., Sarig, Y., Algazi, R.V.: Defect sorting of dry dates by image analysis. Can. Agric. Eng. 35(2), 133–139 (1993)Google Scholar
  9. 9.
    Guyer, D., Uthaisombut, P., Stockman, G.: Tissue reflectance and machine vision for automated sweet cherry sorting. In: Proceedings of the conference SPIE, optics in agriculture, forestry, and biological processing II, pp. 152–165, vol. 2907, Boston (1996)Google Scholar
  10. 10.
    Heinemann, P.H., Hughes, R., Morrow, C.T., Sommer, H.J., Beelman, R.B., Wuest, P.J.: Grading of mushrooms using a machine vision system. Trans. ASAE 37(5), 1671–1677 (1994)CrossRefGoogle Scholar
  11. 11.
    Li, Q., Wang, M., Gu, W.: Computer vision based system for apple surface defect detection. Comput. Electron. Agr. 36(2), 215–223 (2002)CrossRefGoogle Scholar
  12. 12.
    Wen, Z., Tao, Y.: Building a rule-based machine vision system for defect inspection on apple sorting and packing lines. Expert Syst. Appl. 16, 307–313 (1999)CrossRefGoogle Scholar
  13. 13.
    Leemans, V., Destain, M.F.: A real-time grading method of apples based on features extracted from defects. J. Food Eng. 61(1), 83–89 (2004)CrossRefGoogle Scholar
  14. 14.
    Blasco, J., Cubero, S., Gómez Sanchis, J., Mira, P., Moltó, E.: Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. J. Food Eng. 90(1), 27–34 (2009)CrossRefGoogle Scholar
  15. 15.
    Blasco, J., Aleixos, N., Gómez, J., Moltó, E.: Citrus sorting by identification of the most common defects using multispectral computer vision. J. Food Eng. 83(3), 384–393 (2007)CrossRefGoogle Scholar
  16. 16.
    Xiaobo, Z., Jiewen, Z., Yanxiao, L.: Apple color grading based on organization feature parameters. Pattern Recogn. Lett. 28, 2046–2053 (2007)CrossRefGoogle Scholar
  17. 17.
    Abdullah, M.Z., Mohamad Saleh, J., Fathinul Syahir, A.S., Mohd Azemi, B.M.N.: Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system. J. Food Eng. 76(4), 506–523 (2006)CrossRefGoogle Scholar
  18. 18.
    Liming, X., Yanchao, Z.: Automated strawberry grading system based on image processing. Comput. Electron. Agr. 71(S1), S32–S39 (2010)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityChidambaramIndia

Personalised recommendations