1. Universidad Antonio Nariño
  2. Tesis y disertaciones académicas
  3. Doctorados
  4. Doctorado en Ciencia aplicada
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dc.contributor.advisorOrjuela Vargas, Sergio Alejandro-
dc.contributor.advisorHernández Duarte, Andrés Ignacio-
dc.contributor.advisorGutiérrez Salamanca, Rafael María-
dc.creatorVega Diaz, Jhon Jairo-
dc.date.accessioned2021-02-26T14:05:54Z-
dc.date.available2021-02-26T14:05:54Z-
dc.date.created2020-11-25-
dc.identifier.urihttp://repositorio.uan.edu.co/handle/123456789/1972-
dc.descriptionExternaes_ES
dc.description.abstractFor ''Hass'' avocado exportation is required high quality standards, such as fruit maturity. However, the harvested fruit presents heterogeneity of ripeness, requiring a solution that allows identifying the optimal time of harvest. The optimal time is associated with the physiological maturity. And the dry matter (D.M.) content is used to measure the maturity. But, the nondestructive methods to predict its content are very expensive or are not operative. So, we present a solution to identify if a fruit has physiological maturity on the tree. The solution involves a method and a device with two use cases: classification and system parameterization. The classification use case use a support vector machine trained to classify a vector of texture descriptors calculated from an image of the fruit on the tree. The image is acquired in RGB format, without compression and with homogeneous spatial resolution. he image is pre-treated with a contrast limited adaptive histogram equalization and a conversion to an HSV color space. A segment of the HSV image is used to calculate an optimized vector of texture descriptors. The parameterization use case is to optimize the vector of texture descriptors. This vector is a set of textures descriptors with high efficient cross-validation classification and lower computational cost. The device implements the method and must perform the classification in real time, with portability and ruggedness. The main contributions of the process of research, development and innovation are: This research shows that the fruit has a heterogeneous maturity. For that we test all the fruits of a tree in a harvest season, presenting a variation among fruits upper to 20% of D.M. . we prove the viability of the method in a relevant environment, which a classification efficiency of 98.2% that supports the patent application of the proposed solution. And if a farmer implements the invention, he will have an increase of income in 91.4%.es_ES
dc.description.sponsorshipOtroes_ES
dc.description.tableofcontentsPara la exportación de aguacate ''Hass'' se requiere cumplir con altos estándares de calidad, entre los que se destaca la madurez de la fruta. Sin embargo, la fruta cosechada presenta heterogeneidad de maduración y se requiere una solución que permita identificar el momento óptimo de cosecha. El momento óptimo se caracteriza porque la fruta alcanza su madurez fisiológica. Para medir la madurez se usa como referente el contenido de materia seca (M.S.) y los métodos no destructivos parar su predicción son muy costosos o no son operativos. Por lo tanto, se presenta una solución que permite identificar si un fruto tiene madurez fisiológica en el árbol. La solución tiene un método y un dispositivo con dos casos de uso: clasificación y parametrización del sistema. La clasificación usa una máquina de soporte vectorial entrenada para clasificar un vector optimizado de descriptores de textura calculado de una imagen de la fruta en el árbol. La imagen es en formato RGB, sin compresión y con resolución espacial homogénea. La imagen tiene un pretratamiento con la ecualización de histograma adaptativo limitada por contraste y la conversión a un espacio de color HSV. De la imagen HSV se usa un segmento para calcular un vector optimizado de de descriptores de textura. El caso de uso de parametrización es para optimizar el vector de descriptores de textura. Este vector es el conjunto de descriptores que permiten mayor eficacia de clasificación en validación cruzada y un menor costo computacional. El dispositivo implementa el método y debe realizar la clasificación en tiempo real, con portabilidad y robusto. Los principales aportes del proceso de investigación, desarrollo en innovación son: Al evaluar destructivamente todos los frutos de un árbol en temporada de cosecha se demostró que la fruta presenta heterogeneidad de maduración, con una variación entre frutos superior a 20% de M.S.. Se demostró que el método en un ambiente relevante tiene una eficiencia de clasificación del 98.2%, lo cual soporta la solicitud de patente de la solución propuesta. Se proyecta que si un agricultor implementa la invención tendría un aumento de los ingresos en un 91.4%.es_ES
dc.language.isospaes_ES
dc.publisherUniversidad Antonio Nariñoes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.sourceinstname:Universidad Antonio Nariñoes_ES
dc.sourcereponame:Repositorio Institucional UANes_ES
dc.sourceinstname:Universidad Antonio Nariñoes_ES
dc.sourcereponame:Repositorio Institucional UANes_ES
dc.subjectAguacatees_ES
dc.subjectÍndice de cosechaes_ES
dc.subjectMadurez fisiológicaes_ES
dc.subjectDescriptores de texturaes_ES
dc.subjectMáquina de soporte vectoriales_ES
dc.titleInnovación tecnológica para la reducción de la heterogeneidad del aguacate ''Hass'' cosechado para exportaciónes_ES
dc.publisher.programDoctorado en Ciencia Aplicadaes_ES
dc.rights.accesRightsrestrictedAccesses_ES
dc.subject.keywordAvocadoes_ES
dc.subject.keywordHarvest indexes_ES
dc.subject.keywordPhysiological maturityes_ES
dc.subject.keywordTexture descriptorses_ES
dc.subject.keywordSupport vector machinees_ES
dc.type.spaTesis y disertaciones (Maestría y/o Doctorado)es_ES
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES
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dc.description.degreenameDoctor(a) en Ciencia Aplicadaes_ES
dc.description.degreelevelDoctoradoes_ES
dc.publisher.facultyFacultad de Cienciases_ES
dc.description.funderBeca convocatoria 755 colciencias, Gobernación del Tolima, con aval del grupo CEDAGRITOL.es_ES
dc.description.notesPresenciales_ES
dc.creator.orcidhttps://orcid.org/0000-0002-2165-8536es_ES
dc.creator.cvlachttp://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000627968es_ES
dc.creator.googlescholarhttps://scholar.google.com/citations?user=OmCMdXAAAAAJ&hl=es&oi=aoes_ES
dc.creator.cedula93413866es_ES
dc.publisher.campusBogotá - Circunvalar-
Aparece en las colecciones: Doctorado en Ciencia aplicada

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