Mapping gaps in sugarcane by UAV RGB imagery: the lower and earlier the flight, the more accurate

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBarbosa Júnior, Marcelo Rodrigues-
Autor(es): dc.creatorTedesco, Danilo-
Autor(es): dc.creatorCorrêa, Rafael de Graaf-
Autor(es): dc.creatorMoreira, Bruno Rafael de Almeida-
Autor(es): dc.creatorSilva, Rouverson Pereira da-
Autor(es): dc.creatorZerbato, Cristiano-
Data de aceite: dc.date.accessioned2025-08-21T15:31:17Z-
Data de disponibilização: dc.date.available2025-08-21T15:31:17Z-
Data de envio: dc.date.issued2023-03-28-
Data de envio: dc.date.issued2023-03-28-
Data de envio: dc.date.issued2021-12-17-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agronomy11122578-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/242707-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/242707-
Descrição: dc.descriptionImagery data prove useful for mapping gaps in sugarcane. However, if the quality of data is poor or the moment of flying an aerial platform is not compatible to phenology, prediction becomes rather inaccurate. Therefore, we analyzed how the combination of pixel size (3.5, 6.0 and 8.2 cm) and height of plant (0.5, 0.9, 1.0, 1.2 and 1.7 m) could impact the mapping of gaps on unmanned aerial vehicle (UAV) RGB imagery. Both factors significantly influenced mapping. The larger the pixel or plant, the less accurate the prediction. Error was more likely to occur for regions on the field where actively growing vegetation overlapped at gaps of 0.5 m. Hence, even 3.5 cm pixel did not capture them. Overall, pixels of 3.5 cm and plants of 0.5 m outstripped other combinations, making it the most accurate (absolute error ~0.015 m) solution for remote mapping on the field. Our insights are timely and provide forward knowledge that is particularly relevant to progress in the field’s prominence of flying a UAV to map gaps. They will enable producers to make decisions on replanting and fertilizing site-specific high-resolution imagery data.-
Descrição: dc.descriptionNão recebi financiamento-
Descrição: dc.descriptionVersão final do editor-
Descrição: dc.descriptionUniversidade Estadual Paulista (Unesp)-
Descrição: dc.descriptionDepartment of Engineering and Mathematical Sciences, School of Veterinarian and Agricultural Sciences, São Paulo State University (Unesp)-
Descrição: dc.descriptionDepartment of Engineering and Mathematical Sciences School of Veterinarian and Agricultural Sciences São Paulo State University (Unesp)-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMultidisciplinary Digital Publishing Institute-
Relação: dc.relationAgronomy-
Direitos: dc.rightsinfo:eu-repo/semantics/openAccess-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectPixel size-
Palavras-chave: dc.subjectPlant height-
Palavras-chave: dc.subjectRemote sensing-
Palavras-chave: dc.subjectSaccharum spp-
Palavras-chave: dc.subjectUnmanned aerial vehicle-
Palavras-chave: dc.subjectRemote sensing-
Palavras-chave: dc.subjectSugarcane-
Palavras-chave: dc.subjectDrone-
Palavras-chave: dc.subjectCana-de-açúcar-
Título: dc.titleMapping gaps in sugarcane by UAV RGB imagery: the lower and earlier the flight, the more accurate-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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