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Assessment of cluster yield components by image analysis

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Assessment of cluster yield components by image analysis

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dc.contributor.author Diago, Maria P. es_ES
dc.contributor.author Tardaguila, Javier es_ES
dc.contributor.author Aleixos Borrás, María Nuria es_ES
dc.contributor.author Millan, Borja es_ES
dc.contributor.author Prats-Montalbán, José Manuel es_ES
dc.contributor.author Cubero, Sergio es_ES
dc.contributor.author BLASCO IVARS, JOSE es_ES
dc.date.accessioned 2020-10-22T03:32:10Z
dc.date.available 2020-10-22T03:32:10Z
dc.date.issued 2015-04 es_ES
dc.identifier.issn 0022-5142 es_ES
dc.identifier.uri http://hdl.handle.net/10251/152804
dc.description.abstract [EN] BACKGROUND: Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. RESULTS: Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed bymeans of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R2 between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model¿s capability based on image analysis to predict berry weight was 84%. CONCLUSION: The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. © 2014 Society of Chemical Industry es_ES
dc.description.sponsorship This work has been partially funded by INIA through research projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 with the support of European FEDER funds. The authors wish also to thank the MINECO which provided support for this research work through project AGL2011-23673 and also the UPV project UPV-SP10120276 es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation.ispartof Journal of the Science of Food and Agriculture es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Vitis vinifera L es_ES
dc.subject Cluster weight es_ES
dc.subject Berry number per cluster es_ES
dc.subject Berry weight es_ES
dc.subject LIP Canny es_ES
dc.subject Hough Transform es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Assessment of cluster yield components by image analysis es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/jsfa.6819 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//AGL2011-23673/ES/INTEGRACION DE TECNOLOGIAS AVANZADAS DE DETECCION EN UNA PLATAFORMA MOVIL MULTISENSOR PARA EL ESTUDIO DE LA VARIABILIDAD ESPACIO-TEMPORAL DEL VIÑEDO./ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//SP10120276/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RTA2012-00062-C04-01/ES/Nuevas técnicas de inspección basadas en espectrometría para la estimación de propiedades y determinación automática de la calidad interna y sanidad de productos agroalimentarios aplicadas a líneas de inspección y manipulación (SPEC-DACSA)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RTA2012-00062-C04-03/ES/Nuevas técnicas de inspección basadas en visión por computador multiespectral para la estimación de propiedades y determinación automática de la calidad y sanidad de la producción agroalimentaria en líneas de inspección y manipulación (VIS-DACSA)/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Diago, MP.; Tardaguila, J.; Aleixos Borrás, MN.; Millan, B.; Prats-Montalbán, JM.; Cubero, S.; Blasco Ivars, J. (2015). Assessment of cluster yield components by image analysis. Journal of the Science of Food and Agriculture. 95(6):1274-1282. https://doi.org/10.1002/jsfa.6819 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1002/jsfa.6819 es_ES
dc.description.upvformatpinicio 1274 es_ES
dc.description.upvformatpfin 1282 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 95 es_ES
dc.description.issue 6 es_ES
dc.identifier.pmid 25041796 es_ES
dc.relation.pasarela S\268542 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Instituto Nacional de Investigaciones Agrarias es_ES
dc.contributor.funder Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación
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