Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquina

dc.contributor.advisorPrieto Ortiz, Flavio Augusto
dc.contributor.authorRamírez Alberto, Leonardo
dc.contributor.researchgroupGrupo de Automática de la Universidad Nacional GAUNALspa
dc.date.accessioned2021-09-03T03:37:58Z
dc.date.available2021-09-03T03:37:58Z
dc.date.issued2021
dc.descriptionIlustraciones y fotografíasspa
dc.description.abstractAnthracnose 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)eng
dc.description.abstractLa 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).spa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Automatización Industrialspa
dc.description.researchareaVisión de máquina aplicada a la agricultura.spa
dc.format.extentXIX, 82 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80092
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Ingeniería Eléctrica y Electrónicaspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Automatización Industrialspa
dc.relation.indexedAgrosaviaspa
dc.relation.indexedAgrovocspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/spa
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaspa
dc.subject.lembFrutas de huesospa
dc.subject.lembStone fruiteng
dc.subject.proposalVisión por computadoraspa
dc.subject.proposalMonitoreo tempranospa
dc.subject.proposalReducción de pérdidasspa
dc.subject.proposalCaracterización visualspa
dc.subject.proposalMangospa
dc.subject.proposalAntracnosisspa
dc.subject.proposalImágenes 3Dspa
dc.subject.proposalLuz UV-Aspa
dc.subject.proposalComputer Visioneng
dc.subject.proposalEarly Monitoringeng
dc.subject.proposalLoss Reductioneng
dc.subject.proposalVisual Characterizationeng
dc.subject.proposalMangoeng
dc.subject.proposalAnthracnoseeng
dc.subject.proposal3D Imagingeng
dc.subject.proposalUV-A Lighteng
dc.titleDesarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mango basado en visión de máquinaspa
dc.title.translatedDevelopment of a system for early detection of anthracnose in mango fruits based on machine visioneng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
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dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audienceGeneralspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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