Tesis y Trabajos de Investigación PUCP

URI permanente para esta comunidadhttp://54.81.141.168/handle/123456789/6

El Repositorio Digital de Tesis y Trabajos de Investigación PUCP aporta al Repositorio Institucional con todos sus registros, organizados por grado: Doctorado, Maestría, Licenciatura y Bachillerato. Se actualiza permanentemente con las nuevas tesis y trabajos de investigación sustentados y autorizados, así como también con los que que fueron sustentados años atrás.
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Asesorsearch.filters.applied.operator.equals: search.filters.advisor.Racoceanu, DanielTemasearch.filters.applied.operator.equals: search.filters.subject.Análisis espectral--Instrumentos

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    Corn crops identification using multispectral images from unmanned aircraft systems
    (Pontificia Universidad Católica del Perú, 2019-04-08) Trujillano Asato, Fedra Catherine; Racoceanu, Daniel
    Climate change and migration of population from rural to urban areas are affecting the agricultural production around the world. This study was based in the particular department of Ancash - Peru where corn is one of the most important crops of the region. Authorities in this region are concerned in finding a method, different from census; that can constantly monitor corn crops areas. This data is important to evaluate how these two causes will impact on food security in Ancash. The first part of the present thesis reviews the current techniques in the recognition of crop areas using remote sensing and multispectral images. The second part explains the methodology developed for this study, considering the data acquisition using Unmanned Aircraft Systems, the preparation of the acquired data and two deep learning model approaches. The first approach is based on binary classification of corn patches using Le Net model with near infrared images. The second one describes the segmentation of corn areas in different stages using the U-net model, in this case five band images were considered. The third part shows the results of both approaches. From these results it is concluded that training a model with data from different stages and scenarios of two campaigns (2016 and 2017) can achieve a 95% of accuracy in corn segmentation.