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Proceedings Paper

Cocoa bean quality assessment by using hyperspectral images and fuzzy logic techniques
Author(s): Juan Soto; Guillermo Granda; Flavio Prieto; William Ipanaque; Jorge Machacuay
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Paper Abstract

Nowadays, cocoa bean exportation from Piura-Peru is having a positive international market response due to their inherent high quality. Nevertheless, when using subjective techniques for quality assessment, such as the cut test, a wastefulness of grains is generated, additional to a restriction in the selection as well as improvement approaches in earlier stages for optimizing the quality. Thus, in an attempt to standardize the internal features analyzed by the cut test, for instance, crack formation and internal color changes during the fermentation, this research is submitted as an approach which aims to make use of hyperspectral images, with the purpose of having a quick and accurate analysis. Hyperspectral cube size was reduced by using Principal Component Analysis (PCA). The image generated by principal component PC1 provides enough information to clearly distinguish the internal cracks of the cocoa bean, since the zones where these cracks are, have a negative correlation with PC1. The features taken were processed through a fuzzy block, which is able to describe the cocoa bean quality. Three membership functions were defined in the output: unfermented, partly fermented and well fermented, by using trapezoidal-shaped and triangular-shaped functions. A total of twelve rules were propounded. Furthermore, the bisector method was chosen for the defuzzification. Begin the abstract two lines below author names and addresses.

Paper Details

Date Published: 30 April 2015
PDF: 7 pages
Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340M (30 April 2015); doi: 10.1117/12.2182598
Show Author Affiliations
Juan Soto, Univ. de Piura (Peru)
Guillermo Granda, Univ. de Piura (Peru)
Flavio Prieto, Univ. Nacional de Colombia (Colombia)
William Ipanaque, Univ. de Piura (Peru)
Jorge Machacuay, Univ. de Piura (Peru)


Published in SPIE Proceedings Vol. 9534:
Twelfth International Conference on Quality Control by Artificial Vision 2015
Fabrice Meriaudeau; Olivier Aubreton, Editor(s)

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