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Food and Bioprocess Technology

, Volume 6, Issue 2, pp 530–541 | Cite as

Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks

  • Delia Lorente
  • Nuria Aleixos
  • Juan Gómez-Sanchis
  • Sergio Cubero
  • Jose Blasco
Original Paper

Abstract

Early automatic detection of fungal infections in post-harvest citrus fruits is especially important for the citrus industry because only a few infected fruits can spread the infection to a whole batch during operations such as storage or exportation, thus causing great economic losses. Nowadays, this detection is carried out manually by trained workers illuminating the fruit with dangerous ultraviolet lighting. The use of hyperspectral imaging systems makes it possible to advance in the development of systems capable of carrying out this detection process automatically. However, these systems present the disadvantage of generating a huge amount of data, which must be selected in order to achieve a result that is useful to the sector. This work proposes a methodology to select features in multi-class classification problems using the receiver operating characteristic curve, in order to detect rottenness in citrus fruits by means of hyperspectral images. The classifier used is a multilayer perceptron, trained with a new learning algorithm called extreme learning machine. The results are obtained using images of mandarins with the pixels labelled in five different classes: two kinds of sound skin, two kinds of decay and scars. This method yields a reduced set of features and a classification success rate of around 89%.

Keywords

Computer vision Citrus fruits Decay Non-destructive inspection Hyperspectral imaging ROC curve 

Notes

Acknowledgements

This work was partially funded by the Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria de España (INIA) through research project RTA2009-00118-C02-01 and by the Ministerio de Ciencia e Innovación de España (MICINN) through research project DPI2010-19457, both projects with the support of European FEDER funds. This work was also been partially funded by Universitat de València through project UV-INVAE11-41271.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Delia Lorente
    • 1
  • Nuria Aleixos
    • 2
  • Juan Gómez-Sanchis
    • 3
  • Sergio Cubero
    • 1
  • Jose Blasco
    • 1
  1. 1.Centro de AgroingenieríaInstituto Valenciano de Investigaciones Agrarias (IVIA)MoncadaSpain
  2. 2.Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser HumanoUniversitat Politècnica de ValènciaValenciaSpain
  3. 3.Intelligent Data Analysis Laboratory, IDAL, Electronic Engineering DepartmentUniversitat de ValènciaBurjassotSpain

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