Cell-phone based model for the automatic classification of coffee beans defects using white patch

Juan Ramirez-Ticona, Juan Carlos Gutierrez Caceres, Christian E. Portugal-Zambrano

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The classification of physical defects, with the aim of ensure the quality of arabica green coffee beans, is important from a commercial point of view. This classification is done mostly by human experts, which are slow and error prone. The main works in the literature focused on solving the problem using computer vision, require prototypes, which take each image from a completely vertical angle to the surface where the sample is coffee beans. Each of these prototypes is a limiting practical work, because of their difficulty of implementation and the restrictive angle. Seeking a solution to these problems, an automatic sorter twelve physical defects is presented, using images acquired by a cell phone with an angle of diagonal shot, similar to that made by taking a picture of an object located at a lower altitude normally. The classification results show a 100% overall accuracy.

Original languageEnglish
Title of host publicationProceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509016334
DOIs
StatePublished - 25 Jan 2017
Event42nd Latin American Computing Conference, CLEI 2016 - Valparaiso, Chile
Duration: 10 Oct 201614 Oct 2016

Publication series

NameProceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016

Conference

Conference42nd Latin American Computing Conference, CLEI 2016
Country/TerritoryChile
CityValparaiso
Period10/10/1614/10/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • color constancy
  • computer vision
  • green coffee
  • white patch

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