Automatic Identification of Relevant Colors in Non-Destructive Quality Evaluation of Fresh Salad Vegetables

Authors

  • Bernardo Pace Institute of Sciences of Food Production, CNR-National Research Council of Italy Via G. Amendola
  • Dario Pietro Cavallo Institute on Intelligent Systems for Automation, CNR-National Research Council of Italy Via G. Amendola
  • Maria Cefola Institute of Sciences of Food Production, CNR-National Research Council of Italy Via G. Amendola
  • Giovanni Attolico Institute on Intelligent Systems for Automation, CNR-National Research Council of Italy Via G. Amendola

Keywords:

Non-destructive quality evaluation, Relevant colors, Automatic identification, Iceberg head lettuce

Abstract

Quality loss during storage is often associated to changes in relevant product colors and/or to the appearance of new pigments. Computer Vision System (CVS) for non-destructive quality evaluation often relies on human knowledge provided by operators to identify these relevant colors and their features. The approach described in this paper automatically identifies the most significant colors in unevenly colored products to evaluate their quality level. Its performance was compared with results obtained by exploiting human training. The new method improved quality evaluation and reduced the subjectivity and the inconsistency potentially induced by operators.

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Published

2017-02-01

Issue

Vol. 4 No. 1 (2017)

Section

Articles

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