Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/agronomy13030925 http://hdl.handle.net/11449/249828 |
Resumo: | In digital farming, the use of technology to increase agricultural production through automated tasks has recently integrated the development of AgBots for more reliable data collection using autonomous navigation. These AgBots are equipped with various sensors such as GNSS, cameras, and LiDAR, but these sensors can be prone to limitations such as low accuracy for under-canopy navigation with GNSS, sensitivity to outdoor lighting and platform vibration with cameras, and LiDAR occlusion issues. In order to address these limitations and ensure robust autonomous navigation, this paper presents a sensor selection methodology based on the identification of environmental conditions using sensor data. Through the extraction of features from GNSS, images, and point clouds, we are able to determine the feasibility of using each sensor and create a selection vector indicating its viability. Our results demonstrate that the proposed methodology effectively selects between the use of cameras or LiDAR within crops and GNSS outside of crops, at least 87% of the time. The main problem found is that, in the transition from inside to outside and from outside to inside the crop, GNSS features take 20 s to adapt. We compare a variety of classification algorithms in terms of performance and computational cost and the results show that our method has higher performance and lower computational cost. Overall, this methodology allows for the low-cost selection of the most suitable sensor for a given agricultural environment. |
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Repositório Institucional da UNESP |
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Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data IntegrityAgBotsautonomous navigationdigital agricultureensemblemachine learningIn digital farming, the use of technology to increase agricultural production through automated tasks has recently integrated the development of AgBots for more reliable data collection using autonomous navigation. These AgBots are equipped with various sensors such as GNSS, cameras, and LiDAR, but these sensors can be prone to limitations such as low accuracy for under-canopy navigation with GNSS, sensitivity to outdoor lighting and platform vibration with cameras, and LiDAR occlusion issues. In order to address these limitations and ensure robust autonomous navigation, this paper presents a sensor selection methodology based on the identification of environmental conditions using sensor data. Through the extraction of features from GNSS, images, and point clouds, we are able to determine the feasibility of using each sensor and create a selection vector indicating its viability. Our results demonstrate that the proposed methodology effectively selects between the use of cameras or LiDAR within crops and GNSS outside of crops, at least 87% of the time. The main problem found is that, in the transition from inside to outside and from outside to inside the crop, GNSS features take 20 s to adapt. We compare a variety of classification algorithms in terms of performance and computational cost and the results show that our method has higher performance and lower computational cost. Overall, this methodology allows for the low-cost selection of the most suitable sensor for a given agricultural environment.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Sao Carlos School of Engineering University of Sao PauloSchool of Agricultural and Veterinary Studies Sao Paulo State UniversitySchool of Agricultural and Veterinary Studies Sao Paulo State UniversityUniversidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)Bonacini, LeonardoTronco, Mário LuizHiguti, Vitor Akihiro HisanoVelasquez, Andres Eduardo BaqueroGasparino, Mateus ValverdePeres, Handel Emanuel NatividadeOliveira, Rodrigo Praxedes deMedeiros, Vivian SuzanoSilva, Rouverson Pereira da [UNESP]Becker, Marcelo2023-07-29T16:10:16Z2023-07-29T16:10:16Z2023-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/agronomy13030925Agronomy, v. 13, n. 3, 2023.2073-4395http://hdl.handle.net/11449/24982810.3390/agronomy130309252-s2.0-85151750981Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAgronomyinfo:eu-repo/semantics/openAccess2024-06-06T15:18:16Zoai:repositorio.unesp.br:11449/249828Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:10:25.140025Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity |
title |
Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity |
spellingShingle |
Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity Bonacini, Leonardo AgBots autonomous navigation digital agriculture ensemble machine learning |
title_short |
Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity |
title_full |
Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity |
title_fullStr |
Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity |
title_full_unstemmed |
Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity |
title_sort |
Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity |
author |
Bonacini, Leonardo |
author_facet |
Bonacini, Leonardo Tronco, Mário Luiz Higuti, Vitor Akihiro Hisano Velasquez, Andres Eduardo Baquero Gasparino, Mateus Valverde Peres, Handel Emanuel Natividade Oliveira, Rodrigo Praxedes de Medeiros, Vivian Suzano Silva, Rouverson Pereira da [UNESP] Becker, Marcelo |
author_role |
author |
author2 |
Tronco, Mário Luiz Higuti, Vitor Akihiro Hisano Velasquez, Andres Eduardo Baquero Gasparino, Mateus Valverde Peres, Handel Emanuel Natividade Oliveira, Rodrigo Praxedes de Medeiros, Vivian Suzano Silva, Rouverson Pereira da [UNESP] Becker, Marcelo |
author2_role |
author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Bonacini, Leonardo Tronco, Mário Luiz Higuti, Vitor Akihiro Hisano Velasquez, Andres Eduardo Baquero Gasparino, Mateus Valverde Peres, Handel Emanuel Natividade Oliveira, Rodrigo Praxedes de Medeiros, Vivian Suzano Silva, Rouverson Pereira da [UNESP] Becker, Marcelo |
dc.subject.por.fl_str_mv |
AgBots autonomous navigation digital agriculture ensemble machine learning |
topic |
AgBots autonomous navigation digital agriculture ensemble machine learning |
description |
In digital farming, the use of technology to increase agricultural production through automated tasks has recently integrated the development of AgBots for more reliable data collection using autonomous navigation. These AgBots are equipped with various sensors such as GNSS, cameras, and LiDAR, but these sensors can be prone to limitations such as low accuracy for under-canopy navigation with GNSS, sensitivity to outdoor lighting and platform vibration with cameras, and LiDAR occlusion issues. In order to address these limitations and ensure robust autonomous navigation, this paper presents a sensor selection methodology based on the identification of environmental conditions using sensor data. Through the extraction of features from GNSS, images, and point clouds, we are able to determine the feasibility of using each sensor and create a selection vector indicating its viability. Our results demonstrate that the proposed methodology effectively selects between the use of cameras or LiDAR within crops and GNSS outside of crops, at least 87% of the time. The main problem found is that, in the transition from inside to outside and from outside to inside the crop, GNSS features take 20 s to adapt. We compare a variety of classification algorithms in terms of performance and computational cost and the results show that our method has higher performance and lower computational cost. Overall, this methodology allows for the low-cost selection of the most suitable sensor for a given agricultural environment. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T16:10:16Z 2023-07-29T16:10:16Z 2023-03-01 |
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/agronomy13030925 Agronomy, v. 13, n. 3, 2023. 2073-4395 http://hdl.handle.net/11449/249828 10.3390/agronomy13030925 2-s2.0-85151750981 |
url |
http://dx.doi.org/10.3390/agronomy13030925 http://hdl.handle.net/11449/249828 |
identifier_str_mv |
Agronomy, v. 13, n. 3, 2023. 2073-4395 10.3390/agronomy13030925 2-s2.0-85151750981 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Agronomy |
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_ |
1808128614534742016 |