Use este identificador para citar ou acessar este item: https://doi.org/10.48432/MSOSBR
DOI: https://doi.org/10.48432/MSOSBR
Título: Ambient-awareness in agriculture: 3-D structure and reasoning in the crop field (AACr3).
Assunto: Agricultural Sciences;Computer and Information Science;Engineering
Descrição: This dataset was created to enable research on semantic SLAM in agriculture, using a vineyard as a case study. The research goal is the automated recovery of geo-referenced 3-D structure of crop fields and the detection and classification of objects of interest, such as trunks, leaves, and fruits. The instances are readings of different sensors: a color stereo camera, a color 4K camera, a 16-channels LIDAR, a GNSS sensor with ground-base correction (Real-Time Kinematics), and IMUs. All sensors were assembled in a system mounted on a push cart and moved along the rows of two vineyards plots. The plots presented two different growing management systems: vertical shoot positioning growing (VSP) and Y-trellis (YT). This work was funded by the São Paulo Research Foundation (FAPESP) and IBM under grant 17/19282-7, Ambient-awareness in agriculture: 3-D structure and reasoning in the crop field (AACr3). For a complete description of this dataset, see README.md.
Autor(es): Santos, Thiago Teixeira
Koenigkan, Luciano Vieira
URI: https://doi.org/10.48432/MSOSBR
https://www.redape.dados.embrapa.br/dataset.xhtml?amp;persistentId=doi:10.48432/MSOSBR
Outros identificadores:  
Fomento: FAPESP
Número do Projeto: 17/19282-7
Termo de uso:  
Data: 11-Fev-2022
Data de Disponibilização:  
Formato: image/png
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Tipo: Observational data
Editora / Evento / Instituição: Santos, Thiago Teixeira
Idioma : English
Aparece nas coleções:Redape – Repositório de Dados de Pesquisa da Embrapa

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