Olive classification according to external damage using image analysis.

Riquelme Torres, María Teresa and Barreiro Elorza, Pilar and Ruiz-Altisent, Margarita and Barreiro Elorza, Pilar and Valero Ubierna, Constantino (2008). Olive classification according to external damage using image analysis.. "Journal of Food Engineering", v. 87 (n. 3); pp. 371-379. ISSN 0260-8774. https://doi.org/10.1016/j.jfoodeng.2007.12.018.

Description

Title: Olive classification according to external damage using image analysis.
Author/s:
  • Riquelme Torres, María Teresa
  • Barreiro Elorza, Pilar
  • Ruiz-Altisent, Margarita
  • Barreiro Elorza, Pilar
  • Valero Ubierna, Constantino
Item Type: Article
Título de Revista/Publicación: Journal of Food Engineering
Date: August 2008
ISSN: 0260-8774
Volume: 87
Subjects:
Freetext Keywords: Table olives; External damages; Artificial vision; Sorting fruit; Image processing; Perishable products; Postharvest; Information technologies; Motes; Cold chain.
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Ingeniería Rural [hasta 2014]
UPM's Research Group: LPF-TAGRALIA
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The external appearance of an olive’s skin is the most decisive factor in determining its quality as a fruit. This work tries to establish a hierarchical model based on the features extracted from images of olives reflecting their external defects. Seven commercial categories of olives, established by product experts, were used: undamaged olives, mussel-scale or ‘serpeta’, hail-damaged or ‘granizo’, mill or ‘rehús’, wrinkled olive or ‘agostado’, purple olive and undefined-damage or ‘molestado’. The original images were processed using segmentation, colour parameters and morphological features of the defects and the whole fruits. The application of three consecutive discriminant analyses resulted in the correct classification of 97% and 75% of olives during calibration and validation, respectively. However the correct classification percentages vary greatly depending on the categories, ranging 80–100% during calibration and 38– 100% during validation.

More information

Item ID: 2150
DC Identifier: http://oa.upm.es/2150/
OAI Identifier: oai:oa.upm.es:2150
DOI: 10.1016/j.jfoodeng.2007.12.018
Official URL: http://www.elsevier.com/wps/find/journaldescription.cws_home/405862/description#description
Deposited by: Memoria Investigacion
Deposited on: 04 Feb 2010 13:09
Last Modified: 20 Apr 2016 11:54
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