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

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

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Lorente, D.; Aleixos Borrás, MN.; Gómez Sanchís, J.; Cubero, S.; Blasco Ivars, J. (2013). Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks. Food and Bioprocess Technology. 6(2):530-541. doi:10.1007/s11947-011-0737-x

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/68023

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Title: Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària
Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica
Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà
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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 ...[+]
Subjects: Computer vision , Citrus fruits , Decay , Non-destructive inspection , Hyperspectral imaging , ROC curve
Copyrigths: Reserva de todos los derechos
Source:
Food and Bioprocess Technology. (issn: 1935-5130 )
DOI: 10.1007/s11947-011-0737-x
Publisher:
Springer Verlag
Publisher version: http://dx.doi.org/10.1007/s11947-011-0737-x
Thanks:
This work was partially funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA) through research project RTA2009-00118-C02-01 and by the Ministerio de Ciencia e Innovacion de ...[+]
Type: Artículo

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