The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/drones6050112 http://hdl.handle.net/11449/241832 |
Resumo: | Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and tech-nological development into aerial remote sensing for distinguishing cultivars is still at an early stage of development, driving the need for further in-depth investigation. The primary objective of this study was therefore to analyze whether it could be possible to discriminate market-grade cultivars of sugarcane upon imagery data from an unmanned aerial vehicle (UAV). A secondary objective was to analyze whether the time of day could impact the expressiveness of spectral bands and vegetation indices (VIs) in the biophysical modeling. The remote sensing platform acquired high-resolution imagery data, making it possible for discriminating cultivars upon spectral bands and VIs without computational unfeasibility. 12:00 PM especially proved to be the most reliable time of day to perform the flight on the field and model the cultivars upon spectral bands. In contrast, the discrimination upon VIs was not specific to the time of flight. Therefore, this study can provide further information about the division of cultivars of sugarcane merely as a result of processing UAV imagery data. Insights will drive the knowledge necessary to effectively advance the field’s prominence in developing low-altitude, remotely sensing sugarcane. |
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The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicleflight timeNDVIprincipal component analysisreflectanceremote sensingSaccharum sppspectral bandUAVvegetation indexRemote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and tech-nological development into aerial remote sensing for distinguishing cultivars is still at an early stage of development, driving the need for further in-depth investigation. The primary objective of this study was therefore to analyze whether it could be possible to discriminate market-grade cultivars of sugarcane upon imagery data from an unmanned aerial vehicle (UAV). A secondary objective was to analyze whether the time of day could impact the expressiveness of spectral bands and vegetation indices (VIs) in the biophysical modeling. The remote sensing platform acquired high-resolution imagery data, making it possible for discriminating cultivars upon spectral bands and VIs without computational unfeasibility. 12:00 PM especially proved to be the most reliable time of day to perform the flight on the field and model the cultivars upon spectral bands. In contrast, the discrimination upon VIs was not specific to the time of flight. Therefore, this study can provide further information about the division of cultivars of sugarcane merely as a result of processing UAV imagery data. Insights will drive the knowledge necessary to effectively advance the field’s prominence in developing low-altitude, remotely sensing sugarcane.Department of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp), São PauloAgCenter School of Plant Environmental and Soil Sciences Louisiana State UniversityDepartment of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp), São PauloUniversidade Estadual Paulista (UNESP)Louisiana State UniversityBarbosa Júnior, Marcelo Rodrigues [UNESP]Tedesco, Danilo [UNESP]Carreira, Vinicius Dos Santos [UNESP]Pinto, Antonio Alves [UNESP]Moreira, Bruno Rafael de Almeida [UNESP]Shiratsuchi, Luciano ShozoZerbato, Cristiano [UNESP]da Silva, Rouverson Pereira [UNESP]2023-03-02T00:29:48Z2023-03-02T00:29:48Z2022-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/drones6050112Drones, v. 6, n. 5, 2022.2504-446Xhttp://hdl.handle.net/11449/24183210.3390/drones60501122-s2.0-85129920292Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengDronesinfo:eu-repo/semantics/openAccess2024-06-06T15:18:16Zoai:repositorio.unesp.br:11449/241832Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:08:04.568808Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle |
title |
The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle |
spellingShingle |
The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle Barbosa Júnior, Marcelo Rodrigues [UNESP] flight time NDVI principal component analysis reflectance remote sensing Saccharum spp spectral band UAV vegetation index |
title_short |
The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle |
title_full |
The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle |
title_fullStr |
The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle |
title_full_unstemmed |
The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle |
title_sort |
The Time of Day Is Key to Discriminate Cultivars of Sugarcane upon Imagery Data from Unmanned Aerial Vehicle |
author |
Barbosa Júnior, Marcelo Rodrigues [UNESP] |
author_facet |
Barbosa Júnior, Marcelo Rodrigues [UNESP] Tedesco, Danilo [UNESP] Carreira, Vinicius Dos Santos [UNESP] Pinto, Antonio Alves [UNESP] Moreira, Bruno Rafael de Almeida [UNESP] Shiratsuchi, Luciano Shozo Zerbato, Cristiano [UNESP] da Silva, Rouverson Pereira [UNESP] |
author_role |
author |
author2 |
Tedesco, Danilo [UNESP] Carreira, Vinicius Dos Santos [UNESP] Pinto, Antonio Alves [UNESP] Moreira, Bruno Rafael de Almeida [UNESP] Shiratsuchi, Luciano Shozo Zerbato, Cristiano [UNESP] da Silva, Rouverson Pereira [UNESP] |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Louisiana State University |
dc.contributor.author.fl_str_mv |
Barbosa Júnior, Marcelo Rodrigues [UNESP] Tedesco, Danilo [UNESP] Carreira, Vinicius Dos Santos [UNESP] Pinto, Antonio Alves [UNESP] Moreira, Bruno Rafael de Almeida [UNESP] Shiratsuchi, Luciano Shozo Zerbato, Cristiano [UNESP] da Silva, Rouverson Pereira [UNESP] |
dc.subject.por.fl_str_mv |
flight time NDVI principal component analysis reflectance remote sensing Saccharum spp spectral band UAV vegetation index |
topic |
flight time NDVI principal component analysis reflectance remote sensing Saccharum spp spectral band UAV vegetation index |
description |
Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and tech-nological development into aerial remote sensing for distinguishing cultivars is still at an early stage of development, driving the need for further in-depth investigation. The primary objective of this study was therefore to analyze whether it could be possible to discriminate market-grade cultivars of sugarcane upon imagery data from an unmanned aerial vehicle (UAV). A secondary objective was to analyze whether the time of day could impact the expressiveness of spectral bands and vegetation indices (VIs) in the biophysical modeling. The remote sensing platform acquired high-resolution imagery data, making it possible for discriminating cultivars upon spectral bands and VIs without computational unfeasibility. 12:00 PM especially proved to be the most reliable time of day to perform the flight on the field and model the cultivars upon spectral bands. In contrast, the discrimination upon VIs was not specific to the time of flight. Therefore, this study can provide further information about the division of cultivars of sugarcane merely as a result of processing UAV imagery data. Insights will drive the knowledge necessary to effectively advance the field’s prominence in developing low-altitude, remotely sensing sugarcane. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-01 2023-03-02T00:29:48Z 2023-03-02T00:29:48Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/drones6050112 Drones, v. 6, n. 5, 2022. 2504-446X http://hdl.handle.net/11449/241832 10.3390/drones6050112 2-s2.0-85129920292 |
url |
http://dx.doi.org/10.3390/drones6050112 http://hdl.handle.net/11449/241832 |
identifier_str_mv |
Drones, v. 6, n. 5, 2022. 2504-446X 10.3390/drones6050112 2-s2.0-85129920292 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Drones |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128609761624064 |