Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina
Ilustraciones y fotografías
- Autores:
-
Ramírez Alberto, Leonardo
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80092
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Frutas de hueso
Stone fruit
Visión por computadora
Monitoreo temprano
Reducción de pérdidas
Caracterización visual
Mango
Antracnosis
Imágenes 3D
Luz UV-A
Computer Vision
Early Monitoring
Loss Reduction
Visual Characterization
Mango
Anthracnose
3D Imaging
UV-A Light
- Rights
- openAccess
- License
- Atribución-SinDerivadas 4.0 Internacional
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Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina |
| dc.title.translated.eng.fl_str_mv |
Development of a system for early detection of anthracnose in mango fruits based on machine vision |
| title |
Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina |
| spellingShingle |
Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Frutas de hueso Stone fruit Visión por computadora Monitoreo temprano Reducción de pérdidas Caracterización visual Mango Antracnosis Imágenes 3D Luz UV-A Computer Vision Early Monitoring Loss Reduction Visual Characterization Mango Anthracnose 3D Imaging UV-A Light |
| title_short |
Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina |
| title_full |
Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina |
| title_fullStr |
Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina |
| title_full_unstemmed |
Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina |
| title_sort |
Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina |
| dc.creator.fl_str_mv |
Ramírez Alberto, Leonardo |
| dc.contributor.advisor.none.fl_str_mv |
Prieto Ortiz, Flavio Augusto |
| dc.contributor.author.none.fl_str_mv |
Ramírez Alberto, Leonardo |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Automática de la Universidad Nacional GAUNAL |
| 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 Frutas de hueso Stone fruit Visión por computadora Monitoreo temprano Reducción de pérdidas Caracterización visual Mango Antracnosis Imágenes 3D Luz UV-A Computer Vision Early Monitoring Loss Reduction Visual Characterization Mango Anthracnose 3D Imaging UV-A Light |
| dc.subject.lemb.spa.fl_str_mv |
Frutas de hueso |
| dc.subject.lemb.eng.fl_str_mv |
Stone fruit |
| dc.subject.proposal.spa.fl_str_mv |
Visión por computadora Monitoreo temprano Reducción de pérdidas Caracterización visual Mango Antracnosis Imágenes 3D Luz UV-A |
| dc.subject.proposal.eng.fl_str_mv |
Computer Vision Early Monitoring Loss Reduction Visual Characterization Mango Anthracnose 3D Imaging UV-A Light |
| description |
Ilustraciones y fotografías |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-09-03T03:37:58Z |
| dc.date.available.none.fl_str_mv |
2021-09-03T03:37:58Z |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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https://repositorio.unal.edu.co/handle/unal/80092 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/80092 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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Agrosavia Agrovoc |
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Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Prieto Ortiz, Flavio Augustoe5e0629d29d9b754bf18e0f0017122da600Ramírez Alberto, Leonardo2261770942379aae4952f42e07e7bc97Grupo de Automática de la Universidad Nacional GAUNAL2021-09-03T03:37:58Z2021-09-03T03:37:58Z2021https://repositorio.unal.edu.co/handle/unal/80092Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones y fotografíasAnthracnose is the main disease that affects mango fruits, generating up to 100% of losses in the most extreme cases, hence the importance of controlling the disease in mango to avoid reaching such extremes. In this sense, the area of computer vision has had developments to generate non-destructive tools in agriculture, however, there are few works that relate mango and anthracnose by means of CVS and much less for early detection of this disease. In the present work, five methods based on machine vision were developed: Threshold-RGB, RGB-LDA, Threshold-UV, LDA-UV and 3D method, with the purpose of early detection of anthracnose in mango fruits. Results on 30 mangoes indicate that the Threshold-UV and LDA-UV methods can detect the disease 2 days before an expert can do so, according to the severity scale used. This is because UV-A illumination reveals areas of disease that cannot be seen in visible light. The Threshold-RGB and RGB-LDA methods have a performance similar to that of the human eye, since they have the same information. On the other hand, the 3D method does not have a good performance in the detection of the disease, for the test performed, the intersection over junction (IoU) is 47%, which indicates that it is not a good method to determine the disease. Finally, a comparison of the 5 proposed methods using the IoU metric on the same handle is performed, the result of the best segmentations are of the RGB Threshold and LDA-UV methods with values of 0.87 and 0.84, respectively. The lowest performance was 3D segmentation with an average IoU of 0.56. The methods were taken to the field, with unfavorable results, due to uncontrolled factors such as illumination and wind, in addition to the fact that the mangoes evaluated did not show any symptoms of the disease.(Texto tomado de la fuente)La antracnosis es la principal enfermedad que afecta los frutos de mango llegando a generar hasta el 100% de perdidas en los casos más extremos, de ahí la importancia de controlar la enfermedad en mango para no llegar a tales extremos. En este sentido, el área de visión por computadora ha tenido desarrollos para generar herramientas no destructivas en agricultura, sin embargo, son pocos los trabajos que relacionan el mango y la antracnosis por medio de CVS y mucho menos para detectar de forma temprana esta enfermedad. En el presente trabajo se desarrollaron cinco métodos basados en visión de máquina: Umbral-RGB, RGB-LDA, Umbral-UV, LDA-UV y método 3D, con el propósito de detectar la antracnosis de forma temprana en frutos de mango. Los resultados sobre 30 mangos indican que los métodos Umbral-UV y LDA-UV pueden detectar la enfermedad 2 días antes que un experto lo pueda realizar, de acuerdo con la escala de severidad empleada. Lo anterior debido a que la iluminación de luz UV-A que se realiza devela zonas de la enfermedad que en la luz visible no se pueden ver. Los métodos Umbral-RGB y RGB-LDA tienen un desempeño similar al realizado por el ojo humano, esto dado que tienen la misma información. Por otro, lado el método 3D no tiene un buen desempeño en la detección de la enfermedad, para la prueba realizada, la intersección sobre unión (IoU), es de 47 %, lo que indica que no es un buen método para determinar la enfermedad. Finalmente se realiza una comparación de los 5 métodos propuestos empleando la métrica IoU sobre un mismo mango, el resultado de las mejores segmentaciones son de los métodos Umbral RGB y LDA-UV con valores de 0.87 y 0.84, respectivamente. El menor desempeño fue la segmentación 3D con un promedio de IoU de 0.56. Los métodos fueron llevados a campo, con resultados poco favorables, por factores no controlados como la iluminación y el viento, además que los mangos evaluados no debelaban ningún síntoma de la enfermedad. (Texto tomado de la fuente).MaestríaMagíster en Ingeniería - Automatización IndustrialVisión de máquina aplicada a la agricultura.XIX, 82 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaFrutas de huesoStone fruitVisión por computadoraMonitoreo tempranoReducción de pérdidasCaracterización visualMangoAntracnosisImágenes 3DLuz UV-AComputer VisionEarly MonitoringLoss ReductionVisual CharacterizationMangoAnthracnose3D ImagingUV-A LightDesarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquinaDevelopment of a system for early detection of anthracnose in mango fruits based on machine visionTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAgrosaviaAgrovoc[1] I. 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Available:https://www.sciencedirect.com/science/article/pii/S1047320315002035GeneralLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80092/3/license.txtcccfe52f796b7c63423298c2d3365fc6MD53ORIGINAL1022397480.2021.pdf1022397480.2021.pdfTesis de Maestría en Ingeniería - Automatización Industrialapplication/pdf73359641https://repositorio.unal.edu.co/bitstream/unal/80092/4/1022397480.2021.pdf664c8056dc635ec2ef4018e7cc6584ceMD54THUMBNAIL1022397480.2021.pdf.jpg1022397480.2021.pdf.jpgGenerated Thumbnailimage/jpeg4900https://repositorio.unal.edu.co/bitstream/unal/80092/5/1022397480.2021.pdf.jpg32518f2279846d7c3ff00ed302065da3MD55unal/80092oai:repositorio.unal.edu.co:unal/800922023-07-27 23:03:31.586Repositorio Institucional Universidad Nacional de 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