Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo
Aproximadamente el 75% de la superficie agrícola global pertenece a pequeños agricultores, siendo esenciales para el abastecimiento local de alimentos. Sin embargo, los desafíos comunes incluyen la falta de caracterización precisa de los cultivos y la escasa información detallada en las zonas produc...
- Autores:
-
Arregocés Guerra, Paulina
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86302
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
630 - Agricultura y tecnologías relacionadas
Procesamiento de imágenes
Agricultura Inteligente
imágenes aéreas
VANTs
Aprendizaje profundo
Redes Neuronales Convolucionales
Smart Farming
aerial imagery
UAVs
Deep Learning
Convolutional neural networks
Redes neuronales convolucionales
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo |
| dc.title.translated.eng.fl_str_mv |
Method for the classification of small-scale agricultural crops using deep learning techniques |
| title |
Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo |
| spellingShingle |
Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 630 - Agricultura y tecnologías relacionadas Procesamiento de imágenes Agricultura Inteligente imágenes aéreas VANTs Aprendizaje profundo Redes Neuronales Convolucionales Smart Farming aerial imagery UAVs Deep Learning Convolutional neural networks Redes neuronales convolucionales |
| title_short |
Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo |
| title_full |
Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo |
| title_fullStr |
Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo |
| title_full_unstemmed |
Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo |
| title_sort |
Método para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo |
| dc.creator.fl_str_mv |
Arregocés Guerra, Paulina |
| dc.contributor.advisor.none.fl_str_mv |
Branch Bedoya, John Willian Restrepo Arias, Juan Felipe |
| dc.contributor.author.none.fl_str_mv |
Arregocés Guerra, Paulina |
| dc.contributor.researchgroup.spa.fl_str_mv |
Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial |
| dc.contributor.orcid.spa.fl_str_mv |
Arregocés Guerra, Paulina [0000000195670231] |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 630 - Agricultura y tecnologías relacionadas |
| topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación 630 - Agricultura y tecnologías relacionadas Procesamiento de imágenes Agricultura Inteligente imágenes aéreas VANTs Aprendizaje profundo Redes Neuronales Convolucionales Smart Farming aerial imagery UAVs Deep Learning Convolutional neural networks Redes neuronales convolucionales |
| dc.subject.lemb.none.fl_str_mv |
Procesamiento de imágenes |
| dc.subject.proposal.spa.fl_str_mv |
Agricultura Inteligente imágenes aéreas VANTs Aprendizaje profundo Redes Neuronales Convolucionales |
| dc.subject.proposal.eng.fl_str_mv |
Smart Farming aerial imagery UAVs Deep Learning Convolutional neural networks |
| dc.subject.wikidata.none.fl_str_mv |
Redes neuronales convolucionales |
| description |
Aproximadamente el 75% de la superficie agrícola global pertenece a pequeños agricultores, siendo esenciales para el abastecimiento local de alimentos. Sin embargo, los desafíos comunes incluyen la falta de caracterización precisa de los cultivos y la escasa información detallada en las zonas productivas. La Agricultura Inteligente, que utiliza tecnologías avanzadas como Vehículos Aéreos No Tripulados (VANTs) y visión por computadora, ofrece soluciones; sin embargo, su falta de accesibilidad excluye al 94% de los pequeños agricultores en Colombia. Este trabajo aborda la necesidad de proponer un método de clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo. Se utiliza una VANT DJI Mini 2 SE, accesible en el mercado, para capturar imágenes en San Cristóbal, un área rural de Medellín, Colombia, con el objetivo de identificar cultivos de cebolla verde o de rama, follaje y áreas sin cultivo. Con 259 imágenes y 4315 instancias etiquetadas, se emplean modelos de Redes Neuronales Convolucionales (CNNs, por sus siglas en inglés) para la clasificación de objetos, segmentación de instancias y segmentación semántica. Se evaluaron métodos de Aprendizaje Profundo utilizando Transfer Learning, siendo Mask R-CNN el elegido con un 93% de precisión, una tasa de falsos positivos del 9% y falsos negativos del 4%. Las métricas incluyen un porcentaje de precisión promedio medio (mAP%) del 55.49% para follaje, 49.09% para áreas sin cultivo y 58.21% para la cebolla. El conjunto de datos etiquetado está disponible para fomentar la colaboración e investigación comparativa. En términos generales se concluye que mediante la captura de imágenes digitales con VANTs y el uso de métodos de aprendizaje profundo, se puede obtener información precisa y oportuna sobre pequeñas explotaciones agrícolas. (Texto tomado de la fuente) |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-06-25T20:44:08Z |
| dc.date.available.none.fl_str_mv |
2024-06-25T20:44:08Z |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/86302 |
| 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/86302 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.references.spa.fl_str_mv |
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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_abf2Branch Bedoya, John Willian112eaa0bbeeaeb0d3d14dfe15d672a15Restrepo Arias, Juan Felipecf104982d249f92bf4defaced4613e60Arregocés Guerra, Paulina7a421a2bf0f19046f9ec917a395d921fGidia: Grupo de Investigación YyDesarrollo en Inteligencia ArtificialArregocés Guerra, Paulina [0000000195670231]2024-06-25T20:44:08Z2024-06-25T20:44:08Z2024https://repositorio.unal.edu.co/handle/unal/86302Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Aproximadamente el 75% de la superficie agrícola global pertenece a pequeños agricultores, siendo esenciales para el abastecimiento local de alimentos. Sin embargo, los desafíos comunes incluyen la falta de caracterización precisa de los cultivos y la escasa información detallada en las zonas productivas. La Agricultura Inteligente, que utiliza tecnologías avanzadas como Vehículos Aéreos No Tripulados (VANTs) y visión por computadora, ofrece soluciones; sin embargo, su falta de accesibilidad excluye al 94% de los pequeños agricultores en Colombia. Este trabajo aborda la necesidad de proponer un método de clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundo. Se utiliza una VANT DJI Mini 2 SE, accesible en el mercado, para capturar imágenes en San Cristóbal, un área rural de Medellín, Colombia, con el objetivo de identificar cultivos de cebolla verde o de rama, follaje y áreas sin cultivo. Con 259 imágenes y 4315 instancias etiquetadas, se emplean modelos de Redes Neuronales Convolucionales (CNNs, por sus siglas en inglés) para la clasificación de objetos, segmentación de instancias y segmentación semántica. Se evaluaron métodos de Aprendizaje Profundo utilizando Transfer Learning, siendo Mask R-CNN el elegido con un 93% de precisión, una tasa de falsos positivos del 9% y falsos negativos del 4%. Las métricas incluyen un porcentaje de precisión promedio medio (mAP%) del 55.49% para follaje, 49.09% para áreas sin cultivo y 58.21% para la cebolla. El conjunto de datos etiquetado está disponible para fomentar la colaboración e investigación comparativa. En términos generales se concluye que mediante la captura de imágenes digitales con VANTs y el uso de métodos de aprendizaje profundo, se puede obtener información precisa y oportuna sobre pequeñas explotaciones agrícolas. (Texto tomado de la fuente)Approximately 75% of the global agricultural land belongs to small-scale farmers, who are essential for local food supply. However, common challenges include the lack of accurate crop characterization and limited detailed information in productive areas. Smart Farming, employing advanced technologies such as Unmanned Aerial Vehicles (UAVs) and computer vision, offers solutions; however, its lack of accessibility excludes 94% of small-scale farmers in Colombia. This work addresses the need to propose a method for small-scale agricultural crop classification using deep learning techniques. A DJI Mini 2 SE UAV, readily available in the market, is used to capture images in San Cristóbal, a rural area of Medellín, Colombia, with the aim of identifying green onion or branch crops, foliage, and uncultivated areas. With 259 images and 4315 labeled instances, Convolutional Neural Network (CNN) models are employed for object detection, instance segmentation, and semantic segmentation. Deep Learning methods using transfer learning were evaluated, with Mask R-CNN selected, achieving 93% accuracy, a false positive rate of 9%, and false negative rate of 4%. Metrics include an average precision percentage (mAP%) of 55.49% for foliage, 49.09% for uncultivated areas, and 58.21% for onions. The labeled dataset is available to encourage collaboration and comparative research.In general terms, it is concluded that by capturing digital images with UAVs and using deep learning methods, precise and timely information about small agricultural operations can be obtained.MaestríaMagister en Ingeniería AnalíticaÁrea Curricular de Ingeniería de Sistemas e Informática106 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación630 - Agricultura y tecnologías relacionadasProcesamiento de imágenesAgricultura Inteligenteimágenes aéreasVANTsAprendizaje profundoRedes Neuronales ConvolucionalesSmart Farmingaerial imageryUAVsDeep LearningConvolutional neural networksRedes neuronales convolucionalesMétodo para la clasificación de cultivos agrícolas a pequeña escala empleando técnicas de aprendizaje profundoMethod for the classification of small-scale agricultural crops using deep learning techniquesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAgencia de desarrollo rural, FAO, y Gobernación de Antioquia. 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(Cited by: 3) doi: 10.1016/j.biosystemseng.2020.07.013EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86302/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1017232348.2024.pdf1017232348.2024.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf20338222https://repositorio.unal.edu.co/bitstream/unal/86302/2/1017232348.2024.pdf7b88a3a60c69e9d956331da63d77d2e0MD52THUMBNAIL1017232348.2024.pdf.jpg1017232348.2024.pdf.jpgGenerated Thumbnailimage/jpeg4997https://repositorio.unal.edu.co/bitstream/unal/86302/3/1017232348.2024.pdf.jpgb689a272a38aaa83589a4971f19d63b5MD53unal/86302oai:repositorio.unal.edu.co:unal/863022024-06-25 23:05:50.017Repositorio Institucional Universidad Nacional de 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