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dc.contributor.authorChicchón-Apaza, M.Á.
dc.contributor.authorBedón-Monzón, Héctor-Manuel
dc.contributor.otherBedón-Monzón, Héctor-Manuel
dc.contributor.otherChicchón-Apaza, M.Á
dc.date.accessioned2020-04-24T00:27:30Z
dc.date.available2020-04-24T00:27:30Z
dc.date.issued2020
dc.identifier.citationChicchon Azapa, M. & Bedón Monzón, H. (2020). Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net. Communications in Computer and Information Science. 473-485. https://link.springer.com/chapter/10.1007%2F978-3-030-42520-3_38es_PE
dc.identifier.urihttp://repositorio.ulima.edu.pe/handle/ulima/10812
dc.descriptionIndexado en Scopuses_PE
dc.description.abstractA first step in the process of automating weed removal in precision agriculture is the semantic segmentation of crops, weeds and soil. Deep learning techniques based on convolutional neural networks are successfully applied today and one of the most popular network architectures in semantic segmentation problems is U-Net. In this article, the variants in the U-Net architecture were evaluated based on the aggregation of residual and recurring blocks to improve their performance. For training and testing, a set of data available on the Internet was used, consisting of 60 multispectral images with unbalanced pixels, so techniques were applied to increase and balance the data. Experimental results show a slight increase in quality metrics compared to the classic U-Net architecture.es_EN
dc.language.isospaes_PE
dc.publisherSpringeren_EN
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectAutomatizaciónes_PE
dc.subjectAgricultura
dc.subjectRedes neuronales artificiales
dc.subjectAutomation
dc.subjectAgriculture
dc.subjectArtificial neural networks
dc.titleSemantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Netes_PE
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.type.otherArtículo de conferencia en Scopuses_PE
dc.publisher.countryAlemaniaes_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.identifier.doihttps://doi-org.ezproxy.ulima.edu.pe/10.1007/978-3-030-42520-3_38


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