Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos

ilustraciones, fotografías a color

Autores:
Villamizar Marin, Luis Enrique
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/84050
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/84050
https://repositorio.unal.edu.co/
Palabra clave:
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Cultivos y suelos
Productividad del suelo
Manejo de suelos
Crops and soils
Soil productivity
Soil management
Carbono orgánico del suelo
Análisis espectral
Aprendizaje automático
Aprendizaje profundo
Espectroscopia
Reflectancia
Absorbancia
Soil organic carbon
Spectral analysis
Machine learning
Deep learning
Spectroscopy
Reflectance
Absorbance
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_bd2393c29a589afa4e520f0902d9e18d
oai_identifier_str oai:repositorio.unal.edu.co:unal/84050
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos
dc.title.translated.eng.fl_str_mv Contribution of the spectral analysis for the estimation of soil organic carbon in citrus crops
title Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos
spellingShingle Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Cultivos y suelos
Productividad del suelo
Manejo de suelos
Crops and soils
Soil productivity
Soil management
Carbono orgánico del suelo
Análisis espectral
Aprendizaje automático
Aprendizaje profundo
Espectroscopia
Reflectancia
Absorbancia
Soil organic carbon
Spectral analysis
Machine learning
Deep learning
Spectroscopy
Reflectance
Absorbance
title_short Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos
title_full Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos
title_fullStr Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos
title_full_unstemmed Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos
title_sort Aporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricos
dc.creator.fl_str_mv Villamizar Marin, Luis Enrique
dc.contributor.advisor.none.fl_str_mv Prieto Ortiz, Flavio Augusto
Velasquez Hernandez, Carlos Alberto
dc.contributor.author.none.fl_str_mv Villamizar Marin, Luis Enrique
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Automática de la Universidad Nacional Gaunal
dc.contributor.orcid.spa.fl_str_mv Villamizar Marin, Luis Enrique [0009-0001-9837-9703]
dc.contributor.cvlac.spa.fl_str_mv Villamizar Marin, Luis Enrique [0001404535]
dc.contributor.researchgate.spa.fl_str_mv Villamizar, Luis [Luis-Villamizar-5]
dc.contributor.googlescholar.spa.fl_str_mv Villamizar Marin, Luis Enrique [y_Y8qHoAAAAJ]
dc.subject.ddc.spa.fl_str_mv 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Cultivos y suelos
Productividad del suelo
Manejo de suelos
Crops and soils
Soil productivity
Soil management
Carbono orgánico del suelo
Análisis espectral
Aprendizaje automático
Aprendizaje profundo
Espectroscopia
Reflectancia
Absorbancia
Soil organic carbon
Spectral analysis
Machine learning
Deep learning
Spectroscopy
Reflectance
Absorbance
dc.subject.lemb.spa.fl_str_mv Cultivos y suelos
Productividad del suelo
Manejo de suelos
dc.subject.lemb.eng.fl_str_mv Crops and soils
Soil productivity
Soil management
dc.subject.proposal.spa.fl_str_mv Carbono orgánico del suelo
Análisis espectral
Aprendizaje automático
Aprendizaje profundo
Espectroscopia
Reflectancia
Absorbancia
dc.subject.proposal.eng.fl_str_mv Soil organic carbon
Spectral analysis
Machine learning
Deep learning
Spectroscopy
Reflectance
Absorbance
description ilustraciones, fotografías a color
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-06-22T16:18:13Z
dc.date.available.none.fl_str_mv 2023-06-22T16:18:13Z
dc.date.issued.none.fl_str_mv 2023
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/84050
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/84050
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 spa
language spa
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Prieto Ortiz, Flavio Augustoe5e0629d29d9b754bf18e0f0017122daVelasquez Hernandez, Carlos Alberto0ff486ef370abd331bd04e1373f0990aVillamizar Marin, Luis Enrique835a0f6a10470ea0ac040284a123b7ddGrupo de Automática de la Universidad Nacional GaunalVillamizar Marin, Luis Enrique [0009-0001-9837-9703]Villamizar Marin, Luis Enrique [0001404535]Villamizar, Luis [Luis-Villamizar-5]Villamizar Marin, Luis Enrique [y_Y8qHoAAAAJ]2023-06-22T16:18:13Z2023-06-22T16:18:13Z2023https://repositorio.unal.edu.co/handle/unal/84050Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, fotografías a colorEl análisis espectral ha surgido como una alternativa eficiente para el estudio y caracterización de las propiedades del suelo frente a los métodos convencionales. El carbono orgánico del suelo (COS) es un indicador clave para entender el estado del suelo y poder desarrollar prácticas sostenibles del uso del suelo. Este trabajo de investigación evalúa el potencial que tiene el análisis espectral para la estimación del COS en cultivos de cítricos en el municipio de Simacota en el departamento de Santander. Para ello se ajustaron y aplicaron protocolos para la toma de muestras de suelo y para realizar las mediciones espectrales. En total se adquirieron 490 muestras de suelos en la región, a las cuales se les tomaron las firmas espectrales en el rango visible (Vis) de 400 a 900 nm y en el rango del infrarrojo cercano (NIR) de 900 a 2500 nm. Se aplicaron distintos métodos de preprocesamiento a los datos espectrales para mejorar las características espectrales y reducir el ruido, así como métodos de reducción de dimensionalidad, con lo cual se pudieron identificar las longitudes de onda más importantes para la estimación. Se implementaron modelos de aprendizaje automático para la estimación del contenido de COS en los que se incluyeron la regresión de mínimos cuadrados parciales (PLSR), el regresor Cubist y dos modelos basados en redes convolucionales, VGG y Resnet. Los mejores resultados se obtuvieron con PLSR alcanzado un coeficiente de determinación $R^2=0.63$ para el conjunto de validación. Por otra parte, se definieron 2 y 4 grupos a partir del contenido de COS y se implementaron modelos para la clasificación en los que se incluyen los bosques aleatorios (RF), máquinas de vectores de soporte (SVM), clasificador de aumento de gradiente (GB) y los modelos de redes convoluciones configurados para la clasificación. Los mejores resultados de clasificación para 4 grupos alcanzaron una exactitud de 58\% con VGG y de 84\% para la clasificación con 2 grupos con RF. (Texto tomado de la fuente)Spectral analysis has emerged as an efficient alternative for the study and characterization of soil properties compared to conventional methods. Soil organic carbon (SOC) is a key indicator to understand the state of the soil and to be able to develop sustainable land use practices. This research work evaluates the potential of spectral analysis for the estimation of COS in citrus crops in the municipality of Simacota in the department of Santander. To this end, protocols were adjusted and applied for taking soil samples and for performing spectral measurements. In total, 490 soil samples were acquired in the region, from which the spectral signatures were taken in the visible range (Vis) from 400 to 900 nm and in the near infrared range (NIR) from 900 to 2500 nm. Different pre-processing methods were applied to the spectral data to improve spectral characteristics and reduce noise, as well as dimensionality reduction methods, with which the most important wavelengths for the estimation could be identified. Machine learning models were implemented to estimate the COS content, including partial least squares regression (PLSR), the Cubist regressor, and two models based on convolutional networks, VGG and Resnet. The best results were obtained with PLSR reaching a coefficient of determination $R^2=0.63$ for the validation set. On the other hand, 2 and 4 groups were defined based on the COS content and models for classification were implemented, including Random Forests (RF), Support Vector Machines (SVM), Gradient Increase Classifier (GB) and the convolutional network models configured for classification. The best classification results for 4 groups reached an accuracy of 58\% with VGG and 84\% for classification with 2 groups with RF.MaestríaMagíster en Ingeniería - Automatización IndustrialAutomatización Industrialxv, 98 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialFacultad de IngenieríaBogotá,ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaCultivos y suelosProductividad del sueloManejo de suelosCrops and soilsSoil productivitySoil managementCarbono orgánico del sueloAnálisis espectralAprendizaje automáticoAprendizaje profundoEspectroscopiaReflectanciaAbsorbanciaSoil organic carbonSpectral analysisMachine learningDeep learningSpectroscopyReflectanceAbsorbanceAporte del análisis espectral para la estimación de carbono orgánico del suelo en cultivos de cítricosContribution of the spectral analysis for the estimation of soil organic carbon in citrus cropsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMI. 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Demyan, “Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy,” Geoderma, vol. 365, p. 114227, 2020.InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84050/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1094267501.2023.pdf1094267501.2023.pdfTesis de Maestría en Ingeniería - Automatización Industrialapplication/pdf12429826https://repositorio.unal.edu.co/bitstream/unal/84050/2/1094267501.2023.pdf8f998449ecf9fcd130e298f30fbd9823MD52THUMBNAIL1094267501.2023.pdf.jpg1094267501.2023.pdf.jpgGenerated Thumbnailimage/jpeg4574https://repositorio.unal.edu.co/bitstream/unal/84050/3/1094267501.2023.pdf.jpg45c33bacd49ba550d26bb4d7cc0bee60MD53unal/84050oai:repositorio.unal.edu.co:unal/840502023-08-09 23:04:33.794Repositorio Institucional Universidad Nacional de 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