European Food Research and Technology

, Volume 244, Issue 1, pp 161–174 | Cite as

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

  • I. Lopez-Juarez
  • R. Rios-Cabrera
  • S. J. Hsieh
  • M. Howarth
Original Paper
  • 62 Downloads

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.

Keywords

Artificial neural networks Potato quality inspection Machine vision Thermography 

Notes

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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no confict of interest.

Compliance with ethical requirements

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

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Robotics and Advanced ManufacturingCentre for Research and Advanced Studies (CINVESTAV)Ramos ArizpeMexico
  2. 2.Engineering Technology and Industrial Distribution DepartmentTexas A&M UniversityCollege StationUSA
  3. 3.National Centre of Excellence for Food EngineeringSheffield Hallam UniversitySheffieldUK

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