Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage

Datos estadísticos de la FAO y DataBank, indican un aumento en la población subnutrida en los últimos años. Al revisar las cifras, esta problemática se atribuye al crecimiento constante de la población y a una producción de cereales inconstante. En busca de conocer las contribuciones tecnológicas pa...

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Autores:
González Flórez, Miguel Ángel
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2024
Institución:
Universidad de Ibagué
Repositorio:
Repositorio Universidad de Ibagué
Idioma:
spa
OAI Identifier:
oai:repositorio.unibague.edu.co:20.500.12313/4617
Acceso en línea:
https://hdl.handle.net/20.500.12313/4617
Palabra clave:
Enfermedades de planta - Clasificación
Datos PlantVillage
Sintonización de hiperparámetros
Aprendizaje profundo
Clasificación
Enfermedades de plantas
PlantVillage
Hyperparameter tuning
Deep learning
Classification
Plant diseases
PlantVillage
Rights
openAccess
License
http://purl.org/coar/access_right/c_abf2
id UNIBAGUE2_40419ec25cb37f4d30ef8a0f26759133
oai_identifier_str oai:repositorio.unibague.edu.co:20.500.12313/4617
network_acronym_str UNIBAGUE2
network_name_str Repositorio Universidad de Ibagué
repository_id_str
dc.title.spa.fl_str_mv Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage
title Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage
spellingShingle Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage
Enfermedades de planta - Clasificación
Datos PlantVillage
Sintonización de hiperparámetros
Aprendizaje profundo
Clasificación
Enfermedades de plantas
PlantVillage
Hyperparameter tuning
Deep learning
Classification
Plant diseases
PlantVillage
title_short Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage
title_full Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage
title_fullStr Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage
title_full_unstemmed Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage
title_sort Sintonización de hiperparámetros en modelos de aprendizaje profundo para la clasificación de enfermedades en plantas empleando la base de datos PlantVillage
dc.creator.fl_str_mv González Flórez, Miguel Ángel
dc.contributor.advisor.none.fl_str_mv Fernández Gallego, Jose Armando
dc.contributor.author.none.fl_str_mv González Flórez, Miguel Ángel
dc.contributor.jury.none.fl_str_mv Barrero Mendoza, Oscar
dc.subject.armarc.none.fl_str_mv Enfermedades de planta - Clasificación
Datos PlantVillage
topic Enfermedades de planta - Clasificación
Datos PlantVillage
Sintonización de hiperparámetros
Aprendizaje profundo
Clasificación
Enfermedades de plantas
PlantVillage
Hyperparameter tuning
Deep learning
Classification
Plant diseases
PlantVillage
dc.subject.proposal.spa.fl_str_mv Sintonización de hiperparámetros
Aprendizaje profundo
Clasificación
Enfermedades de plantas
PlantVillage
dc.subject.proposal.eng.fl_str_mv Hyperparameter tuning
Deep learning
Classification
Plant diseases
PlantVillage
description Datos estadísticos de la FAO y DataBank, indican un aumento en la población subnutrida en los últimos años. Al revisar las cifras, esta problemática se atribuye al crecimiento constante de la población y a una producción de cereales inconstante. En busca de conocer las contribuciones tecnológicas para la agricultura, se encuentran aportes en agricultura de precisión, robótica, internet de las cosas y visión por computadora. La detección de enfermedades puede contribuir a mejorar el rendimiento de los cultivos y puede ser desarrollada utilizando redes neuronales. En el proceso de entrenamiento de las redes se definen ciertas variables (denominadas hiperparámetros) para configurar la arquitectura de la red neuronal que influyen en diferentes aspectos como la duración del entrenamiento y desempeño de la red. Este trabajo se estudia los hiperparámetros de una red neuronal profunda con el objetivo de mejorar el rendimiento de la red neuronal para clasificar enfermedades en plantas, se realiza un ajuste semiautomático de los hiperparámetros utilizando la librería Keras y la base de datos PlantVillage.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-12-13T23:06:58Z
dc.date.available.none.fl_str_mv 2024-12-13T23:06:58Z
dc.date.issued.none.fl_str_mv 2024
dc.type.none.fl_str_mv Trabajo de grado - Pregrado
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.content.none.fl_str_mv Text
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.redcol.none.fl_str_mv http://purl.org/redcol/resource_type/TP
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_7a1f
status_str acceptedVersion
dc.identifier.citation.none.fl_str_mv González-Flórez, M. A. (2024). Sintonización de Hiperparámetros en Modelos de Aprendizaje Profundo para la Clasificación de Enfermedades en Plantas Empleando la Base de Datos PlantVillage. [Trabajo de grado, Universidad de Ibagué]. https://hdl.handle.net/20.500.12313/4617
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/4617
identifier_str_mv González-Flórez, M. A. (2024). Sintonización de Hiperparámetros en Modelos de Aprendizaje Profundo para la Clasificación de Enfermedades en Plantas Empleando la Base de Datos PlantVillage. [Trabajo de grado, Universidad de Ibagué]. https://hdl.handle.net/20.500.12313/4617
url https://hdl.handle.net/20.500.12313/4617
dc.language.iso.none.fl_str_mv spa
language spa
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spelling Fernández Gallego, Jose Armando13f15360-a12b-4149-9932-b46907b21333-1González Flórez, Miguel Ángel09cb0320-b93a-4a73-9e35-d27f69d1cc0f-1Barrero Mendoza, Oscar2d7e2643-9c20-40f0-a36b-4ecd5bf1e6526002024-12-13T23:06:58Z2024-12-13T23:06:58Z2024Datos estadísticos de la FAO y DataBank, indican un aumento en la población subnutrida en los últimos años. Al revisar las cifras, esta problemática se atribuye al crecimiento constante de la población y a una producción de cereales inconstante. En busca de conocer las contribuciones tecnológicas para la agricultura, se encuentran aportes en agricultura de precisión, robótica, internet de las cosas y visión por computadora. La detección de enfermedades puede contribuir a mejorar el rendimiento de los cultivos y puede ser desarrollada utilizando redes neuronales. En el proceso de entrenamiento de las redes se definen ciertas variables (denominadas hiperparámetros) para configurar la arquitectura de la red neuronal que influyen en diferentes aspectos como la duración del entrenamiento y desempeño de la red. Este trabajo se estudia los hiperparámetros de una red neuronal profunda con el objetivo de mejorar el rendimiento de la red neuronal para clasificar enfermedades en plantas, se realiza un ajuste semiautomático de los hiperparámetros utilizando la librería Keras y la base de datos PlantVillage.Statistical data from FAO and DataBank indicate an increase in the undernourished population in recent years. In reviewing the figures, this problem is attributed to steady population growth and inconsistent cereal production. Looking for technological contributions to agriculture, contributions are found in precision agriculture, robotics, internet of things and computer vision. Disease detection can contribute to improved crop yields and can be developed using neural networks. In the training process of the networks, certain variables (called hyperparameters) are defined to configure the neural network architecture that influence different aspects such as training duration and network performance. This work studies the hyperparameters of a deep neural network with the aim of improving the performance of the neural network to classify plant diseases. A semi-automatic adjustment of the hyperparameters is performed using the Keras library and the PlantVillage dataset.PregradoIngeniero ElectrónicoResumen . . . . . III Lista de Figuras . . . . . VIII Lista de Tablas . . . . . IX Lista de Algoritmos . . . . . X Lista de Abreviaturas . . . . . XII 1. Introducción . . . . . 1 1.1. Descripción del Problema . . . . . 1 1.2. Objetivos de Investigación . . . . . 3 1.2.1. Objetivo General . . . . . 3 1.2.2. Objetivos Específicos . . . . . 3 2. Marco Teórico . . . . . 4 2.1. Aprendizaje Profundo . . . . . 4 2.2. Redes Neuronales . . . . . 5 2.2.1. Redes Neuronales Convolucionales . . . . . 6 2.2.1.1. ResNet-50 . . . . . 8 2.3. Entrenamiento, Validación y Prueba . . . . . 9 2.4. Hiperparámetros . . . . . 10 2.4.1. Tamaño del Lote . . . . . 10 2.4.2. Función de Pérdida . . . . . 11 2.4.2.1. Entropía Categórica Cruzada . . . . . 11 2.4.3. Optimizador . . . . . 11 2.4.3.1. SGD . . . . . 12 2.4.3.2. RMSprop . . . . . 13 2.4.3.3. Adam . . . . . 13 2.4.4. Tasa de Aprendizaje . . . . . 14 2.5. Métricas de Evaluación . . . . . 15 2.5.1. Precisión . . . . . 16 2.5.2. Exhaustividad . . . . . 16 2.5.3. Puntuación F1 . . . . . 17 2.5.4. Exactitud . . . . . 18 2.6. Técnicas para la Mejora del Rendimiento . . . . . 18 2.6.1. Aumento de Datos . . . . . 18 2.6.2. Aprendizaje por Transferencia . . . . . 19 2.6.3. Sintonización de Hiperparámetros . . . . . 20 2.6.3.1. Búsqueda en Cuadrícula . . . . . 20 2.6.3.2. Búsqueda Aleatoria . . . . . 21 2.6.3.3. Optimización Bayesiana . . . . . 21 2.6.3.4. HiperBanda . . . . . 22 3. Estado del Arte . . . . . 23 3.1. Trabajos Comparativos . . . . . 23 3.2. Trabajos Enfocados a una Técnica . . . . . 24 3.2.1. Búsqueda en Cuadrícula e HiperBanda . . . . . 24 3.2.2. Búsqueda Aleatoria . . . . . 24 3.2.3. Optimización Bayesiana . . . . . 25 4. Materiales . . . . . 26 4.1. Computadora . . . . . 26 4.1.1. Aprovechamiento de la GPU . . . . . 26 4.2. Plataforma de Desarrollo . . . . . 27 4.3. Librerías . . . . . 28 4.3.1. Propósitos Generales . . . . . 28 4.3.2. Sintonización de Hiperparámetros . . . . . 28 4.4. Base de Datos . . . . . 29 5. Metodología . . . . . 32 5.1. Selección de la Arquitectura . . . . . 32 5.2. Selección del Espacio de Búsqueda . . . . . 33 5.3. Sintonización de Hiperparámetros . . . . . 34 5.3.1. Implementación del HyperModel . . . . . 34 5.3.2. Implementación y Adecuación de los Tuner . . . . . 35 5.3.3. Búsqueda de Hiperparámetros . . . . . 39 5.4. Evaluación de los Algoritmos . . . . . 39 5.4.1. Evaluación de Entrenamiento y Validación . . . . . 39 5.4.1.1. Gráfico del Espacio de Búsqueda . . . . . 40 5.4.1.2. Gráfico de Coordenadas Paralelas . . . . . 40 5.4.1.3. Tiempo de Búsqueda . . . . . 41 5.4.1.4. Curvas de Pérdida y Exactitud . . . . . 41 5.4.2. Evaluación de la Prueba . . . . . 42 5.4.2.1. Gráfico de Radar . . . . . 42 6. Resultados y Discusión . . . . . 44 6.1. Resultados . . . . . 44 6.1.1. Rendimiento según el Espacio de Búsqueda . . . . . 44 6.1.2. Rendimiento según Hiperparámetros . . . . . 45 6.1.3. Rendimiento según Tiempo . . . . . 46 6.1.4. Rendimiento según Aprendizaje . . . . . 47 6.1.5. Rendimiento según Métricas de Evaluación . . . . . 47 6.2. Discusión . . . . . 49 6.2.1. Resultados de Entrenamiento y Validación . . . . . 49 6.2.1.1. Exactitud por Configuración . . . . . 49 6.2.1.2. Tiempos de Entrenamiento . . . . . 49 6.2.1.3. Tiempos de Búsqueda . . . . . 50 6.2.1.4. Curvas de Aprendizaje . . . . . 50 6.2.2. Resultados de la Prueba . . . . . 51 7. Conclusiones y Trabajo Futuro . . . . . 52 7.1. Conclusiones . . . . . 52 7.2. Trabajo Futuro . . . . . 53 Anexos . . . . . 54 A. Estado del Arte de Hyper-Tuning . . . . . 54 B. Descripción de PlantVillage Dataset . . . . . 56 C. Estado del Arte de Arquitecturas . . . . . 58 D. Función run_trial() General . . . . . 60 Bibliografía . . . . . 7387 páginasapplication/pdfGonzález-Flórez, M. A. (2024). Sintonización de Hiperparámetros en Modelos de Aprendizaje Profundo para la Clasificación de Enfermedades en Plantas Empleando la Base de Datos PlantVillage. 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