Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBonacini, Leonardo-
Autor(es): dc.creatorTronco, Mário Luiz-
Autor(es): dc.creatorHiguti, Vitor Akihiro Hisano-
Autor(es): dc.creatorVelasquez, Andres Eduardo Baquero-
Autor(es): dc.creatorGasparino, Mateus Valverde-
Autor(es): dc.creatorPeres, Handel Emanuel Natividade-
Autor(es): dc.creatorOliveira, Rodrigo Praxedes de-
Autor(es): dc.creatorMedeiros, Vivian Suzano-
Autor(es): dc.creatorSilva, Rouverson Pereira da-
Autor(es): dc.creatorBecker, Marcelo-
Data de aceite: dc.date.accessioned2025-08-21T17:02:36Z-
Data de disponibilização: dc.date.available2025-08-21T17:02:36Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agronomy13030925-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249828-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249828-
Descrição: dc.descriptionIn 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.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionSao Carlos School of Engineering University of Sao Paulo-
Descrição: dc.descriptionSchool of Agricultural and Veterinary Studies Sao Paulo State University-
Descrição: dc.descriptionSchool of Agricultural and Veterinary Studies Sao Paulo State University-
Idioma: dc.languageen-
Relação: dc.relationAgronomy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAgBots-
Palavras-chave: dc.subjectautonomous navigation-
Palavras-chave: dc.subjectdigital agriculture-
Palavras-chave: dc.subjectensemble-
Palavras-chave: dc.subjectmachine learning-
Título: dc.titleSelection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.