Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas

ilustraciones, diagramas, mapas

Autores:
Calderón Caro, Evelin
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
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spa
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oai:repositorio.unal.edu.co:unal/83615
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https://repositorio.unal.edu.co/handle/unal/83615
https://repositorio.unal.edu.co/
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000 - Ciencias de la computación, información y obras generales
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales
Tecnología agrícola
Agricultura - Tecnología apropiada
Pronóstico
Redes neuronales artificiales
Temperatura mínima
Variables climáticas
Forecast
Artificial neural networks
Minimum temperature
Climatic variables
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Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_989ae0433db986e70a2f7f00e260674c
oai_identifier_str oai:repositorio.unal.edu.co:unal/83615
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas
dc.title.translated.eng.fl_str_mv Early prediction of Frost events in high altitude crops, using machine learning methods
title Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas
spellingShingle Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas
000 - Ciencias de la computación, información y obras generales
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales
Tecnología agrícola
Agricultura - Tecnología apropiada
Pronóstico
Redes neuronales artificiales
Temperatura mínima
Variables climáticas
Forecast
Artificial neural networks
Minimum temperature
Climatic variables
title_short Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas
title_full Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas
title_fullStr Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas
title_full_unstemmed Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas
title_sort Predicción temprana de heladas en cultivos de altura, empleando métodos de aprendizaje de máquinas
dc.creator.fl_str_mv Calderón Caro, Evelin
dc.contributor.advisor.none.fl_str_mv Castañeda Sánchez, Darío Antonio
Branch Bedoya, John Willian
dc.contributor.author.none.fl_str_mv Calderón Caro, Evelin
dc.contributor.researchgroup.spa.fl_str_mv Gidia: Grupo de Investigación y Desarrollo en Inteligencia Artificial
dc.contributor.orcid.spa.fl_str_mv Calderón Caro, Evelin [0000-0002-9754-0905]
dc.subject.ddc.spa.fl_str_mv 000 - Ciencias de la computación, información y obras generales
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales
topic 000 - Ciencias de la computación, información y obras generales
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales
Tecnología agrícola
Agricultura - Tecnología apropiada
Pronóstico
Redes neuronales artificiales
Temperatura mínima
Variables climáticas
Forecast
Artificial neural networks
Minimum temperature
Climatic variables
dc.subject.agrovoc.spa.fl_str_mv Tecnología agrícola
dc.subject.lemb.spa.fl_str_mv Agricultura - Tecnología apropiada
dc.subject.proposal.spa.fl_str_mv Pronóstico
Redes neuronales artificiales
Temperatura mínima
Variables climáticas
dc.subject.proposal.eng.fl_str_mv Forecast
Artificial neural networks
Minimum temperature
Climatic variables
description ilustraciones, diagramas, mapas
publishDate 2022
dc.date.issued.none.fl_str_mv 2022
dc.date.accessioned.none.fl_str_mv 2023-03-13T13:34:28Z
dc.date.available.none.fl_str_mv 2023-03-13T13:34:28Z
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/83615
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/83615
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
dc.relation.indexed.spa.fl_str_mv RedCol
LaReferencia
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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_abf2Castañeda Sánchez, Darío Antonio1b0aaaebb495e222ca90235c2c42aa4d600Branch Bedoya, John Willian8373bc4285cc9e2e59e8f540f737e1db600Calderón Caro, Evelin97644420ca5a9be61de4bf3ce0d5db02600Gidia: Grupo de Investigación y Desarrollo en Inteligencia ArtificialCalderón Caro, Evelin [0000-0002-9754-0905]2023-03-13T13:34:28Z2023-03-13T13:34:28Z2022https://repositorio.unal.edu.co/handle/unal/83615Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, mapasEn Colombia, muchos cultivos están ubicados en los altiplanos de las regiones andinas, en altitudes superiores a 2.500 m s.n.m, donde se concentra la mayor susceptibilidad a la ocurrencia de eventos de heladas. El objetivo de este estudio fue proponer un modelo de predicción temprana de heladas basado en la relación entre estos eventos y variables climáticas, mediante la implementación de algoritmos de aprendizaje de máquinas. Las variables climáticas se obtuvieron a partir de trece estaciones meteorológicas distribuidas en nueve municipios del departamento de Cundinamarca. Las variables registradas fueron la temperatura, humedad relativa, punto de rocío, radiación fotosintéticamente activa y precipitación, estas constituyeron las variables explicativas de los eventos de heladas. Las métricas utilizadas para la evaluación predictiva del rendimiento de los cinco métodos de aprendizaje de máquinas examinados fueron precisión, tasa de verdaderos positivos, tasa de verdaderos negativos, exactitud y puntuación F1. Se identificó que las horas previas a la ocurrencia de un evento de helada se caracterizan por presentar baja humedad, bajo punto de rocío y alta radiación. Cuatro de los cinco modelos entrenados se desempeñaron satisfactoriamente, con métricas de evaluación superiores al 91 %. La validación cruzada y el análisis estadístico demostraron que el modelo de potenciación del gradiente para la detección de heladas presentó la mayor precisión. Adicionalmente, se evaluaron dos modelos para la predicción de la temperatura mínima y se encontraron métricas de error (error medio absoluto y error cuadrático medio) inferiores a 0,55 °C para una ventana de tiempo de una hora. (Texto tomado de la fuente)In Colombia, many crops are located in the highlands of the Andean region, at altitudes above 2,500 m a.s.l., where the greatest susceptibility to the occurrence of frost events is concentrated. The objective of this study was to propose an early frost prediction model based on the relationship between these events and climatic variables, through the implementation of machine learning algorithms. The climatic variables were obtained from thirteen meteorological stations distributed in nine municipalities of the department of Cundinamarca. The variables recorded were temperature, relative humidity, dew point, photosynthetically active radiation, and precipitation, these constituted the explanatory variables of frost events. The metrics used for the predictive evaluation of the performance of the five machine learning methods examined were precision, true positive rate, true negative rate, accuracy, and F1 score. It was identified that the hours prior to the occurrence of a frost event were characterized by low humidity, low dew point and high radiation. Four of the five trained models performed satisfactorily, with evaluation metrics greater than 91 %. Cross-validation and statistical analysis showed that the gradient boosting model for frost detection had the highest accuracy. 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Informatics in Medicine Unlocked, 17(March), 100179. https://doi.org/10.1016/j.imu.2019.100179Soluciones Wiga S.A.SGrowers Hub TradingEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/83615/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1152707862.2023.pdf1152707862.2023.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf2792358https://repositorio.unal.edu.co/bitstream/unal/83615/2/1152707862.2023.pdfcff7de4cb33a06182e4de35fe42c8890MD52THUMBNAIL1152707862.2023.pdf.jpg1152707862.2023.pdf.jpgGenerated Thumbnailimage/jpeg5178https://repositorio.unal.edu.co/bitstream/unal/83615/3/1152707862.2023.pdf.jpg1574c0c6c58e8f27fd854047acb65c19MD53unal/83615oai:repositorio.unal.edu.co:unal/836152024-07-25 23:14:37.101Repositorio Institucional Universidad Nacional de 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