Método para la estimación de maleza en cultivos de lechuga utilizando aprendizaje profundo e imágenes multiespectrales

dc.contributorPedraza Bonilla, César Augusto
dc.contributorRodríguez Mújica, Leonardo
dc.contributorPLaS - Programming Languages and Systems
dc.creatorOsorio Delgado, Anderson Kavir
dc.date.accessioned2021-10-11T15:05:06Z
dc.date.available2021-10-11T15:05:06Z
dc.date.created2021-10-11T15:05:06Z
dc.date.issued2021-09-13
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/80482
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractLa estimación de maleza es una de las tareas más importantes durante el proceso de control de maleza, pues de esta depende la estimación de costos que deberán emplearse para proteger el cultivo, por tanto este trabajo presenta el desarrollo de un método que utiliza imágenes multiespectrales capturadas por un vehículo aéreo no tripulado y redes neuronales convolucionales para realizar la estimación porcentual de maleza en cultivos de lechuga. El método presentado tiene una exactitud del 89% y un valor-F de 94% para la detección del cultivo, con un tiempo de ejecución promedio de 0.4 segundos sin GPU y una correlación de 0.57 en la evaluación de cobertura de maleza en relación con un Ph.D en malherbología. Estos resultados indican que la tarea de estimación de maleza usando CNNs es más precisa y rápida que la realizada por expertos, pero sin alejarse del conocimiento tácito el cual es importante en la estimación de costos y recursos para el control de la maleza. (Texto tomado de la fuente).
dc.description.abstractWeed estimation is one of the most important tasks during the weed control process. The estimation of costs to be used to protect the crop depends on it. Therefore, this work presents the development of a method, which uses multispectral images captured by an unmanned aerial vehicle and convolutional neural networks. In order to perform percentage quantification of weeds in lettuce crops. The presented method has an accuracy of 89% and an F-value of 94% for crop detection. Its average run time is 0.4 seconds without GPU. In addition to a correlation of 0.57 in the weed cover assessment in relation to a Ph.D. weed science expert. These results indicate that the weed estimation task using CNNs is more accurate and faster than that performed by experts. But without departing from the tacit knowledge which is important in estimating costs and resources for weed control.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisherDepartamento de Ingeniería de Sistemas e Industrial
dc.publisherFacultad de Ingeniería
dc.publisherBogotá, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleMétodo para la estimación de maleza en cultivos de lechuga utilizando aprendizaje profundo e imágenes multiespectrales
dc.typeTrabajo de grado - Maestría


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