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Evaluación de herramientas de análisis de datos espectrales para la identificación y cuantificación de la madurez temprana en papa

dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.advisorRamírez Gil, Joaquín Guillermo
dc.contributor.advisorGómez Caro, Sandra
dc.contributor.authorLeon Rueda, William Alfonso
dc.date.accessioned2023-08-08T15:31:11Z
dc.date.available2023-08-08T15:31:11Z
dc.date.issued2023-08-02
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/84484
dc.descriptionilustraciones, fotografías, diagramas, mapas
dc.description.abstractEl cultivo de papa es afectado por diferentes enfermedades que disminuyen su rendimiento, entre ellas, los problemas asociados a madurez temprana (MT) causada por Verticillium spp. han cobrado importancia en Colombia. La falta de estrategias de manejo y en especial herramientas de diagnóstico y detección temprana ha generado la necesidad de identificar técnicas de detección indirecta con aplicación potencial a nivel comercial. Por lo anterior, este trabajo tuvo como objetivo evaluar herramientas de análisis de datos espectrales para la identificación y cuantificación de MT asociada a Verticillium spp. en cultivos de papa. El trabajo se dividió en dos fases en busca de caracterizar a nivel espectral plantas sanas y enfermas, además de hacer una aproximación a la cuantificación indirecta de distintos niveles de severidad de la enfermedad. En primer lugar, se compararon firmas espectrales adquiridas mediante un espectro radiómetro fijo bajo condiciones controladas con el fin de identificar bandas e índices espectrales contrastantes por su capacidad para la detección y cuantificación indirecta de esta patología. Posteriormente, en dos áreas de producción comercial se generaron clasificaciones utilizando algoritmos de aprendizaje automático (Bosques aleatorios, Máquinas de soporte vectorial, Redes neuronales y Adaboost), seleccionando aquellos de mejor comportamiento mediante parámetros de rendimiento por su capacidad para la identificación de plantas sanas y enfermas. Adicionalmente, se realizó una aproximación a la cuantificación de la severidad usando datos multiespectrales adquiridos por medio de un dron. Los resultados indican que los algoritmos usados no tuvieron diferencias significativas entre la capacidad de clasificación usando como predictoras firmas espectrales de plantas sanas y enfermas. Igualmente, las regiones del rojo y el borde rojo fueron las que presentaron mayor importancia en los clasificadores, conllevando a que los índices espectrales RECI, NDRE y GRVI presentaron mayor capacidad discriminatoria. En cuanto a los lotes comerciales, se observó que las clasificaciones alcanzaron niveles aceptables de exactitud, los cuales están directamente relacionados con las variables de intensidad de la enfermedad. Por otra parte, se resalta que en esta propuesta se hace un vínculo entre firmas espectrales e imágenes multiespectrales adquiridas bajo condiciones controladas y tomados en cultivos de condición comercial campo, hallando regiones e índices espectrales informativos con un alto potencial para el desarrollo de sensores ópticos de bajo costo que permitan la detección indirecta de la MT en el cultivo de papa. (Texto tomado de la fuente)
dc.description.abstractPotato crop is affected by different diseases that reduce yield, among them, problems associated with early maturity (MT) caused by Verticillium spp. have gained importance in Colombia. The lack of management strategies, especially diagnostic and early detection tools, has generated the need to identify indirect detection techniques with potential commercial application. Therefore, the objective of this work was to evaluate spectral data analysis tools for the identification and quantification of MT associated with Verticillium spp. in potato crops. The work was divided into two phases in order to characterize healthy and diseased plants at the spectral level, as well as to make an approximation to the indirect quantification of different levels of disease severity. First, spectral signatures acquired by means of a fixed radiometer spectrum were compared under controlled conditions in order to identify contrasting spectral bands and indices for their capacity for the detection and indirect quantification of this pathology. Subsequently, in two commercial production areas, classifications were generated using machine learning algorithms (Random Forests, Support Vector Machines, Neural Networks and Adaboost), selecting those with the best performance parameters for their ability to identify healthy and diseased plants. Additionally, a severity quantification approach was performed using multispectral data acquired from a drone. The results indicate that the algorithms used had no significant differences between the classification capability using spectral signatures of healthy and diseased plants as predictors. Likewise, the red and red-edge regions were those that presented the greatest importance in the classifiers, leading to the RECI, NDRE and GRVI spectral indices presenting greater discriminatory capacity. As for the commercial lots, it was observed that the classifications reached acceptable levels of accuracy, which are directly related to the variables of disease intensity. On the other hand, it is highlighted that in this proposal a link is made between spectral signatures and multispectral images acquired under controlled conditions and taken in commercial field condition crops, finding regions and informative spectral indices with a high potential for the development of lowcost optical sensors that allow the indirect detection of MT in potato crops.
dc.format.extent135 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleEvaluación de herramientas de análisis de datos espectrales para la identificación y cuantificación de la madurez temprana en papa
dc.typeTrabajo de grado - Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ciencias Agrarias - Maestría en Geomática
dc.contributor.researchgroupBiogénesis
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Geomática
dc.description.researchareaTecnologías Geoespaciales
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.facultyFacultad de Ciencias Agrarias
dc.publisher.placeBogotá, Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.agrovocAnálisis de datos
dc.subject.agrovocdata analysis
dc.subject.agrovocMadurez
dc.subject.agrovocmaturity
dc.subject.agrovocVerticillium
dc.subject.proposalMétodos de clasificación
dc.subject.proposalSensores remotos
dc.subject.proposalAprendizaje automático
dc.subject.proposalBandas espectrales informativas
dc.subject.proposalDetección indirecta de enfermedades
dc.subject.proposalDetección indirecta
dc.subject.proposalClassification methods
dc.subject.proposalRemote sensing
dc.subject.proposalMachine learning
dc.subject.proposalInformative spectral bands
dc.subject.proposalIndirect detection
dc.title.translatedEvaluation of spectral data analysis tools for the identification and quantification of early maturity in potato
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
oaire.awardtitleEstudio de Verticillium y de una patología de origen desconocido en papa: aproximación desde la detección, epidemiología, manejo e importancia económica
oaire.fundernameFEDEPAPA - FNFP
dcterms.audience.professionaldevelopmentEstudiantes
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentPúblico general
dc.contributor.orcidWilliam Alfonso Leon Rueda [0000000310511093]


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