Assessment of chestnut (Castanea spp.) slice quality using color images

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dc.contributor.authorDonis Gonzalez, Irwin R.
dc.contributor.authorGuyer, Daniel E.
dc.contributor.authorLeiva Valenzuela, Gabriel A.
dc.contributor.authorBurns, James
dc.date.accessioned2024-01-10T12:04:21Z
dc.date.available2024-01-10T12:04:21Z
dc.date.issued2013
dc.description.abstractUnbiased internal quality classification of Chestnuts (Castanea spp.) is extremely important to the fresh and processed industries. It can also be used as a tool for applied scientific studies, such as the training of non-invasive techniques to determine chestnut internal quality, and the effect of pre- and post-harvest treatments. At the moment, humans visually perform the invasive quality assessment of chestnuts. This procedure is prone to errors and high variability due to individuals' fatigue, lack of training, and subjectivity. Thus, there is a need to develop a technique that is able to objectively classify internal quality of chestnuts. In this paper, a computer vision methodology is proposed to sort chestnuts into five classes, as established by an expert human rater. 1790 color images from slices with different quality classes were acquired, using a flat panel scanner, from the hybrid cultivar 'Colossal' and 'Chinese seedlings'. After preprocessing, a total of 1931 color, textural, and geometric features were extracted from each color image. Furthermore, the most relevant features were selected using a sequential forward selection algorithm. Thirty-six features were found to be effective in designing a quadratic discriminant classifier with a cross-validated overall performance accuracy of 89.6%. These results showed that this method is an accurate, reliable, and objective tool to determine chestnut slice quality, and might be applicable to in-line sorting systems. (C) 2012 Elsevier Ltd. All rights reserved.
dc.description.funderErnie and Mabel Rogers Endowment
dc.description.funderProject GREEEN at Michigan State University
dc.fechaingreso.objetodigital27-03-2024
dc.format.extent8 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.jfoodeng.2012.09.017
dc.identifier.eissn1873-5770
dc.identifier.issn0260-8774
dc.identifier.urihttps://doi.org/10.1016/j.jfoodeng.2012.09.017
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75771
dc.identifier.wosidWOS:000314620500017
dc.information.autorucIngeniería;Leiva-Valenzuela H;S/I;189461
dc.issue.numero3
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final414
dc.pagina.inicio407
dc.publisherELSEVIER SCI LTD
dc.revistaJOURNAL OF FOOD ENGINEERING
dc.rightsacceso restringido
dc.subjectClassification
dc.subjectComputer vision
dc.subjectPattern recognition
dc.subjectProcessed chestnuts
dc.subjectINVARIANT TEXTURE CLASSIFICATION
dc.subjectCOMPUTER VISION
dc.subjectPATTERN-RECOGNITION
dc.subjectCHEESE
dc.subject.ods02 Zero Hunger
dc.subject.odspa02 Hambre cero
dc.titleAssessment of chestnut (Castanea spp.) slice quality using color images
dc.typeartículo
dc.volumen115
sipa.codpersvinculados189461
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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