Extraction of morphological and spectral features of potato plants from high resolution multispectral images

ilustraciones, fotografías, gráficas, tablas

Autores:: Rodríguez Galvis, Jorge Luis

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Extraction of morphological and spectral features of potato plants from high resolution multispectral images
dc.title.translated.spa.fl_str_mv	Extracción de características morfológicas y espectrales de plantas de papa a partir de imágenes multiespectrales de alta resolución
title	Extraction of morphological and spectral features of potato plants from high resolution multispectral images
spellingShingle	Extraction of morphological and spectral features of potato plants from high resolution multispectral images 000 - Ciencias de la computación, información y obras generales Enfermedades de las plantas Plant diseases Papa Potatoes Vigilancia de plagas Pest monitoring Procesamiento de datos Data processing Late blight UAV Remote sensing Machine learning Plant traits Tizón tardío Percepción remota Aprendizaje de maquina Rasgos de plantas Aeronave remotamente tripulada
title_short	Extraction of morphological and spectral features of potato plants from high resolution multispectral images
title_full	Extraction of morphological and spectral features of potato plants from high resolution multispectral images
title_fullStr	Extraction of morphological and spectral features of potato plants from high resolution multispectral images
title_full_unstemmed	Extraction of morphological and spectral features of potato plants from high resolution multispectral images
title_sort	Extraction of morphological and spectral features of potato plants from high resolution multispectral images
dc.creator.fl_str_mv	Rodríguez Galvis, Jorge Luis
dc.contributor.advisor.none.fl_str_mv	Lizarazo Salcedo, Iván Alberto Prieto Ortíz, Flavio Augusto
dc.contributor.author.none.fl_str_mv	Rodríguez Galvis, Jorge Luis
dc.contributor.researchgroup.spa.fl_str_mv	Análisis Espacial del Territorio y del Cambio Global (AET-CG) Grupo de Automática de la Universidad Nacional GAUNAL
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales
topic	000 - Ciencias de la computación, información y obras generales Enfermedades de las plantas Plant diseases Papa Potatoes Vigilancia de plagas Pest monitoring Procesamiento de datos Data processing Late blight UAV Remote sensing Machine learning Plant traits Tizón tardío Percepción remota Aprendizaje de maquina Rasgos de plantas Aeronave remotamente tripulada
dc.subject.agrovoc.none.fl_str_mv	Enfermedades de las plantas Plant diseases Papa Potatoes Vigilancia de plagas Pest monitoring Procesamiento de datos Data processing
dc.subject.proposal.eng.fl_str_mv	Late blight UAV Remote sensing Machine learning Plant traits
dc.subject.proposal.spa.fl_str_mv	Tizón tardío Percepción remota Aprendizaje de maquina Rasgos de plantas Aeronave remotamente tripulada
description	ilustraciones, fotografías, gráficas, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-08-26T16:52:33Z
dc.date.available.none.fl_str_mv	2021-08-26T16:52:33Z
dc.date.issued.none.fl_str_mv	2021-03-16
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80029
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80029 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.rights.spa.fl_str_mv	Derechos reservados al autor, 2021
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-CompartirIgual 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-CompartirIgual 4.0 Internacional Derechos reservados al autor, 2021 http://creativecommons.org/licenses/by-sa/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	X, 82 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias Agrarias - Maestría en Geomática
dc.publisher.department.spa.fl_str_mv	Escuela de posgrados
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Agrarias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-CompartirIgual 4.0 InternacionalDerechos reservados al autor, 2021http://creativecommons.org/licenses/by-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lizarazo Salcedo, Iván Albertob7a911d83f30c19f50a8d2f7b4e94e02Prieto Ortíz, Flavio Augusto13ffc51d2d98b7ee93bf77c8adaaee2dRodríguez Galvis, Jorge Luis919b8684f8069cd549c801a4897abf56Análisis Espacial del Territorio y del Cambio Global (AET-CG)Grupo de Automática de la Universidad Nacional GAUNAL2021-08-26T16:52:33Z2021-08-26T16:52:33Z2021-03-16https://repositorio.unal.edu.co/handle/unal/80029Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías, gráficas, tablasEste trabajo estudia el uso de características espectrales y morfológicas en la evaluación y detección del tizón tardío de la papa utilizando imágenes multiespectrales de muy alta resolución capturadas por vehículos aéreos no tripulados (UAV). Los métodos tradicionales de detección y cartografía del tizón tardío consumen mucho tiempo, requieren un gran esfuerzo humano y, en muchos casos, son subjetivos. El estudio de las características geométricas y espectrales de las plantas de papa mediante UAV puede contribuir a mejorar la eficiencia de los sistemas de detección de campo que se utilizan actualmente. Esta investigación busca contribuir a la determinación de los métodos de captura, procesamiento y análisis de los datos adquiridos a través de UAV de manera que proporcione a los productores herramientas confiables para el mejoramiento y manejo de sus cultivos de manera ágil y eficiente. El enfoque de este estudio integra operaciones morfológicas y evalúa el rendimiento de cinco algoritmos de aprendizaje automático: bosque aleatorio (RF), clasificador de aumento de gradiente (GBC), clasificador de vectores de soporte (SVC), clasificador de vectores de soporte lineal (LSVC) y K- vecinos más cercanos. (KNN) para detectar áreas de tizón tardío. Los principales componentes del enfoque propuesto son: (i) corrección radiométrica y geométrica de imágenes en bruto; (ii) eliminación del suelo desnudo mediante la aplicación de una técnica de umbralización; (iii) la generación de índices espectrales; (iv) la construcción de características morfológicas de las plantas; (v) un procedimiento de clasificación supervisado utilizando algoritmos de ML; y (vi) uso de modelos previamente entrenados para clasificar un nuevo conjunto de datos. El desempeño del método se evalúa en dos fechas en un campo de papa experimental. Los resultados mostraron que los clasificadores LSVC y RF se desempeñaron mejor en términos de métricas de precisión y tiempo de ejecución. El estudio mostró que el método propuesto permite la detección del tizón tardío con poca intervención humana. (Texto tomado de la fuente)This work studies the use of spectral and morphological features in the evaluation and detection of potato late blight using very high resolution multispectral images captured by Unmanned Aerial Vehicles (UAV). Traditional late blight detection and mapping methods are time-consuming, require great human effort and, in many cases, are subjective. The study of the geometric and spectral characteristics of potato plants by means of UAV can contribute to improving the efficiency of the field detection systems that are currently used. This research seeks to contribute to the determination of the capture, processing and analysis methods of the data acquired through UAV in a way that provides producers with reliable tools for the improvement and management of their crops in an agile and efficient way. The approach of this study integrates morphological operations and evaluates the performance of five machine learning algorithms: Random Forest (RF), Gradient Boosting classifier (GBC), Support Vector Classifier (SVC), Linear Support Vector Classifier (LSVC) and K- Nearest Neighbours (KNN) to detect late blight areas. The main components of the proposed approach are: (i) radiometric and geometric correction of raw images; (ii) elimination of bare soil by applying a thresholding technique; (iii) the generation of spectral indices; (iv) the construction of morphological features of the plants; (v) a supervised classification procedure using ML algorithms; and (vi) use of pre-trained models to classify a new data set. The performance of the method is evaluated on two dates in an experimental potato field. The results showed that the LSVC and RF classifiers performed the best in terms of accuracy and execution time metrics. The study showed that the proposed method allows the detection of late blight with little human intervention.MaestríaMagíster en GeomáticaTecnologías GeoespacialesX, 82 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en GeomáticaEscuela de posgradosFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generalesEnfermedades de las plantasPlant diseasesPapaPotatoesVigilancia de plagasPest monitoringProcesamiento de datosData processingLate blightUAVRemote sensingMachine learningPlant traitsTizón tardíoPercepción remotaAprendizaje de maquinaRasgos de plantasAeronave remotamente tripuladaExtraction of morphological and spectral features of potato plants from high resolution multispectral imagesExtracción de características morfológicas y espectrales de plantas de papa a partir de imágenes multiespectrales de alta resoluciónTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAn, N., Palmer, C. 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Extraction of morphological and spectral features of potato plants from high resolution multispectral images

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