Comparison of laser backscattering imaging and computer vision system for grading of seedless watermelons

  • Maimunah Mohd AliEmail author
  • Norhashila Hashim
  • Siti Khairunniza Bejo
  • Rosnah Shamsudin
Original Paper


The potential of the computer vision system and backscattering imaging with a laser diode emitting at 658 nm was investigated for evaluating color changes and grading of seedless watermelons. A total of 80 seedless watermelons were selected for predicting color changes of the fruit. The watermelons were stored at 10 °C (85% RH) for 21 days with four storage days interval; Day 0, Day 8, Day 15, and Day 21. Image segmentation process and partial least squares (PLS) regression were conducted for both Red, Green, and Blue (RGB) and backscattering image analysis. Prediction models for color changes were developed based on the RGB and backscattering parameters extracted from the watermelon images. Linear discriminant analysis (LDA) was used to compare the classification models of the computer vision system and backscattering imaging based on the storage days. The results showed that the backscattering imaging classified different storage days better than computer vision system, with the classification accuracy higher than 94%. In conclusion, this work has demonstrated the ability of backscattering imaging coupled with the laser diode as a non-destructive method for color evaluation and grading of seedless watermelons.


Computer vision Backscattering imaging Watermelons Color evaluation Grading 



The authors are grateful for the support received from the Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia for providing the facilities for this study from the Ministry of Higher Education, Malaysia under a Fundamental Research Grant Scheme (FRGS) (Vot. No.: 5524604).

Compliance with ethical standards

Conflict of interest

The authors declare that no conflict of interest exists.


  1. 1.
    B. Zhang, W. Huang, J. Li, C. Zhao, S. Fan, J. Wu, C. Liu, Food Res. Int. 62, 326–343 (2014)CrossRefGoogle Scholar
  2. 2.
    C. Garrido-Novell, D. Pérez-Marin, J.M. Amigo, J. Fernández-Novales, J.E. Guerrero, A. Garrido-Varo, J. Food Eng. 113, 281–288 (2012)CrossRefGoogle Scholar
  3. 3.
    M.T. Sánchez, I. Torres, M.J. De la Haba, D. Pérez-Marín, Biosyst. Eng. 123, 12–18 (2014)CrossRefGoogle Scholar
  4. 4.
    P. Perkins-Veazie, J.K. Collins, Postharvest Biol. Technol. 31, 159–166 (2004)CrossRefGoogle Scholar
  5. 5.
    C. Zheng, D.-W. Sun, L. Zheng, Trends Food Sci. Technol. 17, 642–655 (2006)CrossRefGoogle Scholar
  6. 6.
    K.K. Patel, A. Kar, S.N. Jha, M.A. Khan, J. Food Sci. Technol. 49(2), 123–141 (2012)CrossRefGoogle Scholar
  7. 7.
    P.B. Pathare, U.L. Opara, F.A.J. Al-Said, Food Bioprocess. Technol. 6, 36–60 (2013)CrossRefGoogle Scholar
  8. 8.
    D. Wu, D.W. Sun, Trends Food Sci. Technol. 29, 5–20 (2013)CrossRefGoogle Scholar
  9. 9.
    M. Taghizadeh, A.A. Gowen, C.P. O’Donnell, Biosyst. Eng. 108, 191–194 (2011)CrossRefGoogle Scholar
  10. 10.
    A. Manickavasagan, N.K. Al-Mezeini, H.N. Al-Shekaili, Sci. Hortic. (Amsterdam) 175, 87–94 (2014)CrossRefGoogle Scholar
  11. 11.
    N. Hashim, R.B. Janius, R.A. Rahman, A. Osman, Pertanika. J. Sci. Technol. 21(1), 111–118 (2013)Google Scholar
  12. 12.
    G. Romano, D. Argyropoulos, M. Nagle, M.T. Khan, J. Müller, J. Food Eng. 109, 438–448 (2012)CrossRefGoogle Scholar
  13. 13.
    S.E. Adebayo, N. Hashim, K. Abdan, M. Hanafi, J. Food Eng. 169, 155–164 (2016)CrossRefGoogle Scholar
  14. 14.
    M. Mohd Ali, N. Hashim, S.K. Bejo, R. Shamsudin, Postharvest Biol. Technol. 123, 1–59 (2017)CrossRefGoogle Scholar
  15. 15.
    K. Mollazade, M. Omid, F.A. Tab, S.S. Mohtasebi, Food Bioprocess Technol. 5(5), 1465–1485 (2012)CrossRefGoogle Scholar
  16. 16.
    Y. Rezaei Kalaj, K. Mollazade, W. Herppich, C. Regen, M. Geyer, Sci. Hortic. (Amsterdam) 202, 63–69 (2016)CrossRefGoogle Scholar
  17. 17.
    G. Romano, M. Nagle, D. Argyropoulos, J. Müller, J. Food Eng. 104, 657 (2011)CrossRefGoogle Scholar
  18. 18.
    Z. Qing, B. Ji, M. Zude, Postharvest Biol. Technol. 48(2), 215–222 (2008)CrossRefGoogle Scholar
  19. 19.
    P. Udomkun, M. Nagle, B. Mahayothee, J. Müller, Food Control 42, 225–233 (2014)CrossRefGoogle Scholar
  20. 20.
    S.E. Adebayo, N. Hashim, K. Abdan, M. Hanafi, K. Mollazade, Sci. Hortic. (Amsterdam) 212, 171–182 (2016)CrossRefGoogle Scholar
  21. 21.
    D.L. Dénes, V. Parrag, J. Felföldi, L. Baranyai, J. Food Phys. 26, 11–16 (2013)Google Scholar
  22. 22.
    N. Hashim, R.B. Janius, R. Abdul, A. Osman, M. Shitan, M. Zude, J. Eng. Sci. Technol. 9, 314–325 (2014)Google Scholar
  23. 23.
    S. Babazadeh, P. Ahmadi Moghaddam, A. Sabatyan, F. Sharifian, Comput. Electron. Agric. 129, 1–8 (2016)CrossRefGoogle Scholar
  24. 24.
    S. Cubero, N. Aleixos, E. Moltó, J. Gómez-Sanchis, J. Blasco, Food Bioprocess. Technol. 4(4), 487–504 (2011)CrossRefGoogle Scholar
  25. 25.
    C. Liu, W. Liu, X. Lu, W. Chen, J. Yang, L. Zheng, Food Chem. 195, 110–116 (2016)CrossRefGoogle Scholar
  26. 26.
    C.H. Trinderup, Y.H.B. Kim, Food Res. Int. 71, 100–107 (2015)CrossRefGoogle Scholar
  27. 27.
    M. Zhang, J. De Baerdemaeker, E. Schrevens, Food Res. Int. 36, 669–676 (2003)CrossRefGoogle Scholar
  28. 28.
    J.-L. Xu, D.-W. Sun, Int. J. Refrig. 74, 149–162 (2017)Google Scholar
  29. 29.
    I. Doymaz, P. Altiner, Food Sci. Biotechnol. 21, 43–49 (2012)CrossRefGoogle Scholar
  30. 30.
    F. Mendoza, R. Lu, D. Ariana, H. Cen, B. Bailey, Postharvest Biol. Technol. 62(2), 149–160 (2011)Google Scholar
  31. 31.
    M. Mohd Ali, N. Hashim, S. K. Bejo, R. Shamsudin, J. Food Sci. Technol. 54(11), 3650–3657 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biological and Agricultural Engineering, Faculty of EngineeringUniversiti Putra MalaysiaSerdangMalaysia
  2. 2.Department of Food and Process Engineering, Faculty of EngineeringUniversiti Putra MalaysiaSerdangMalaysia

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