Advertisement

Calyx and Stem Discrimination for Apple Quality Control Using Hyperspectral Imaging

  • Israel PinedaEmail author
  • Nur Alam MD
  • Oubong Gwun
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)

Abstract

The production of high-quality food products needs an efficient method to detect defects in food, this is particularly true in the production of apples. Hyperspectral image processing is a popular technique to carry out this detection. However, the stem and calyx of the apple provoke frequent detection errors. We analyze the spectrum of our apple data set, propose an algorithm that uses the average of the principal components of two regions of the spectrum to identify the defects, and couple this detection routine with a two-band ratio that discriminates the calyx and stem. Our study considers the spectral range between 403 nm and 998 nm. Our results include the detection of scab, bruise, crack, and cut with and without stem and calyx. We describe all the necessary parameters provided by our spectral analysis. Our algorithm has an overall accuracy of 95%. We conclude that our algorithm effectively detects defects in the presence of stem and calyx.

Keywords

Hyperspectral imaging Two-band ratio Defect detection 

References

  1. 1.
    ElMasry, G., Wang, N., Vigneault, C.: Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks. Postharvest Biol. Technol. 52(1), 1–8 (2009).  https://doi.org/10.1016/J.POSTHARVBIO.2008.11.008, https://www.sciencedirect.com/science/article/pii/S0925521408003220CrossRefGoogle Scholar
  2. 2.
    Lee, H., et al.: A simple multispectral imaging algorithm for detection of defects on red delicious apples. J. Biosyst. Eng. 39(2), 142–149 (2014).  https://doi.org/10.5307/JBE.2014.39.2.142, http://koreascience.or.kr/journal/view.jsp?kj=NOGGB5&py=2014&vnc=v39n2&sp=142CrossRefGoogle Scholar
  3. 3.
    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).  https://doi.org/10.1016/S0260-8774(03)00189-4, https://www.sciencedirect.com/science/article/pii/S0260877403001894CrossRefGoogle Scholar
  4. 4.
    Li, J., et al.: Multispectral detection of skin defects of bi-colored peaches based on vis-NIR hyperspectral imaging. Postharvest Biol. Technol. 112, 121–133 (2016).  https://doi.org/10.1016/j.postharvbio.2015.10.007CrossRefGoogle Scholar
  5. 5.
    Li, J., Rao, X., Ying, Y.: Detection of common defects on oranges using hyperspectral reflectance imaging. Comput. Electron. Agric. 78(1), 38–48 (2011).  https://doi.org/10.1016/J.COMPAG.2011.05.010, https://www.sciencedirect.com/science/article/pii/S0168169911001256CrossRefGoogle Scholar
  6. 6.
    López-García, F., Andreu-García, G., Blasco, J., Aleixos, N., Valiente, J.M.: Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Comput. Electron. Agric. 71(2), 189–197 (2010).  https://doi.org/10.1016/J.COMPAG.2010.02.001, https://www.sciencedirect.com/science/article/pii/S016816991000013XCrossRefGoogle Scholar
  7. 7.
    Mehl, P.M., Chen, Y.R., Kim, M.S., Chan, D.E.: Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J. Food Eng. 61(1), 67–81 (2004).  https://doi.org/10.1016/S0260-8774(03)00188-2, https://www.sciencedirect.com/science/article/pii/S0260877403001882CrossRefGoogle Scholar
  8. 8.
    Merken, P., Vandersmissen, R.: Dark current and influence of target emissivity. Technical report (2016)Google Scholar
  9. 9.
    Polder, G., van der Heijden, G.W., Keizer, L.P., Young, I.T.: Calibration and characterisation of imaging spectrographs. J. Near Infrared Spectrosc. 11(3), 193–210 (2003). https://doi.org/10.1255/jnirs.366, http://journals.sagepub.com/doi/10.1255/jnirs.366CrossRefGoogle Scholar
  10. 10.
    Qin, J., Burks, T.F., Zhao, X., Niphadkar, N., Ritenour, M.A.: Development of a two-band spectral imaging system for real-time citrus canker detection. J. Food Eng. 108(1), 87–93 (2012).  https://doi.org/10.1016/j.jfoodeng.2011.07.022CrossRefGoogle Scholar
  11. 11.
    Wang, J., Nakano, K., Ohashi, S., Kubota, Y., Takizawa, K., Sasaki, Y.: Detection of external insect infestations in jujube fruit using hyperspectral reflectance imaging. Biosyst. Eng. 108(4), 345–351 (2011).  https://doi.org/10.1016/J.BIOSYSTEMSENG.2011.01.006, https://www.sciencedirect.com/science/article/pii/S1537511011000183CrossRefGoogle 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(3), 307–313 (1999).  https://doi.org/10.1016/S0957-4174(98)00079-7, https://www.sciencedirect.com/science/article/pii/S0957417498000797CrossRefGoogle Scholar
  13. 13.
    Zhang, B., et al.: Hyperspectral imaging combined with multivariate analysis and band math for detection of common defects on peaches (prunus persica). Comput. Electron. Agric. 114, 14–24 (2015).  https://doi.org/10.1016/J.COMPAG.2015.03.015, https://www.sciencedirect.com/science/article/pii/S0168169915001003CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ecuador Metropolitan UniversityQuitoEcuador
  2. 2.Chonbuk National UniversityJeonjuSouth Korea

Personalised recommendations