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A hybrid non-invasive method for internal/external quality assessment of potatoes

Abstract

Consumers purchase fruits and vegetables based on its quality, which can be defined as a degree of excellence which is the result of a combination of characteristics, attributes and properties that have significance for market acceptability. In this paper, a novel hybrid active imaging methodology for potato quality inspection that uses an optical colour camera and an infrared thermal camera is presented. The methodology employs an artificial neural network (ANN) that uses quality data composed by two descriptors as input. The ANN works as a feature classifier so that its output is the potato quality grade. The input vector contains information related to external characteristics, such as shape, weight, length and width. Internal characteristics are also accounted for in the input vector in the form of excessive sugar content. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The validation of the methodology was made through experimentation which consisted in fusing both, external and internal characteristics in the input vector to the ANN for an overall quality classification. Results using internal data as obtained from an infrared camera and fused with optical external parameters demonstrated the feasibility of the method since the prediction accuracy increased during potato grading.

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References

  1. 1.

    Food and agriculture data (2016) http://faostat3.fao.org/. Visited 20 Dec 2016

  2. 2.

    Rios-Cabrera R, Lopez-Juarez I, Hsieh SJ (2008) ANN analysis in a vision approach for potato inspection. J Appl Res Technol 6:106–119

    Google Scholar 

  3. 3.

    Krishnamurthy K, Khurana HK, Soojin J, Irudayaraj J, Demirci A (2008) Infrared heating in food processing: an overview. Rev Food Sci Food Saf 7:2–13

    Article  Google Scholar 

  4. 4.

    Zhou L, Chalana V, Kim Y (1998) PC-based machine vision system for real-time computer-aided potato inspection. Int J Imaging Syst Technol 9:423–433

    Article  Google Scholar 

  5. 5.

    Noordam JC, Otten GW, Timmermans TJM, van Zwol BH (2000) High-speed potato grading and quality inspection based on a color vision system. Proc SPIE 3966:206–217

    Article  Google Scholar 

  6. 6.

    Rady AM, Guyer DE (2015) Rapid and/or nondestructive quality evaluation methods for potatoes: a review. Comput Electron Agric 117:31–48

    Article  Google Scholar 

  7. 7.

    Santos Gomes JF, Rodrigues Leta F (2012) Applications of computer vision techniques in the agriculture and food industry: a review. Eur Food Res Technol 235:989–1000

    Article  Google Scholar 

  8. 8.

    Liu Y, Ying Y, Fu X, Lu H (2007) Experiments on predicting sugar content in apples by FT-NIR technique. J Food Eng 80:986–989

    CAS  Article  Google Scholar 

  9. 9.

    Hsieh C, Lee Y (2005) Applied visible/near-infrared spectroscopy on detecting the sugar content and hardness of pearl Guava. Appl Eng Agric 21:1039–1046

    Article  Google Scholar 

  10. 10.

    Schaare PN, McGlone VA, Oliver RJ, Clark CJ (2012) Using visible/near infrared spectroscopy to assess soluble solids content of grapes on a moving conveyor. In: Proc ASABE

  11. 11.

    Haase NU (2011) Prediction of potato processing quality by near infrared reflectance spectroscopy of ground raw tubers. J Near Infrared Spectrosc 19:37–45

    CAS  Article  Google Scholar 

  12. 12.

    Rady AM, Guyer DE (2015) Evaluation of sugar content in potatoes using NIR reflectance and wavelength selection techniques. Postharvest Biol Technol 103:17–26

    CAS  Article  Google Scholar 

  13. 13.

    Gowen AA, Tiwari BK, Cullen PJ, McDonnell K, O’Donnell CP (2010) Applications of thermal imaging in food quality and safety assessment. Trends Food Sci Technol 21:190–200

    CAS  Article  Google Scholar 

  14. 14.

    Lopez-Juarez I, Rios-Cabrera R, Peña-Cabrera M, Osorio-Comparan R (2010) Learning and fast object recognition in robot skill acquisition: a new method. In: Martínez-Trinidad JF, Carrasco-Ochoa JA, Kittler J (eds) Advances in pattern recognition. MCPR 2010. Lecture notes in computer science, vol 6256. Springer, Berlin, Heidelberg, pp 40–49

  15. 15.

    Lopez-Juarez I, Castelan M, Castro-Martinez FJ, Peña-Cabrera M, Osorio-Comparan R (2013) Using object’s contour, form and depth to embed recognition capability into industrial robots. J Appl Res Technol 11:5–17

    Article  Google Scholar 

  16. 16.

    Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1992) Fuzzy ARTMAP: a neural network architecture for incremental learning of analog multidimensional maps. IEEE Trans Neural Netw 3:698–713

    CAS  Article  Google Scholar 

  17. 17.

    Martens S, Gaudiano P, Carpenter GA (1998) Mobile robot sensor integration with Fuzzy ARTMAP. In: Proc IEEE/ISIC CIRA/ISAS joint conference, pp 307–312

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Acknowledgements

The authors want to acknowledge the financial support for this research provided by the Texas A&M University-CONACyT: Collaborative Research Grant Program. Thanks are also due to CONACyT and the Sheffield Hallam University for the support provided to Dr. Lopez-Juarez during his sabbatical leave.

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Correspondence to I. Lopez-Juarez.

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This research does not contain any studies with human or animal subjects.

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Lopez-Juarez, I., Rios-Cabrera, R., Hsieh, S.J. et al. A hybrid non-invasive method for internal/external quality assessment of potatoes. Eur Food Res Technol 244, 161–174 (2018). https://doi.org/10.1007/s00217-017-2936-9

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Keywords

  • Artificial neural networks
  • Potato quality inspection
  • Machine vision
  • Thermography