Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Instrumentação. |
Data corrente: |
19/12/2017 |
Data da última atualização: |
21/01/2020 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
SANTOS, T. T.; BASSOI, L. H.; OLDONI, H.; MARTINS, R. L. |
Afiliação: |
THIAGO TEIXEIRA SANTOS, CNPTIA; LUIS HENRIQUE BASSOI, CNPDIA; HENRIQUE OLDONI, Unesp Botucatu; ROBERTO LUVISUTTO MARTINS, Unesp Botucatu. |
Título: |
Automatic grape bunch detection in vineyards based on affordable 3D phenotyping using a consumer webcam. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 11., 2017, Campinas. Ciência de dados na era da agricultura digital: anais. Campinas: Editora da Unicamp: Embrapa Informática Agropecuária, 2017. |
Páginas: |
p. 89-98. |
ISBN: |
978-85-85783-75-4 |
Idioma: |
Inglês |
Notas: |
SBIAgro 2017. |
Conteúdo: |
This work presents a methodology for 3-D phenotyping of vineyards based on images captured by a low cost high-definition webcamera. A novel software application integrated visual odometry and multiple-view stereo components to create dense and accurate three-dimensional points clouds for vines, properly transformed to millimeter scale. Geometrical and color features of the points were employed by a classification procedure that reached 93% of accuracy on detecting points belonging to grapes. Individual bunches were automatically delimited and their volumes estimated. The sum of the estimated volumes per vine presented a coefficient of correlation of R = 0.99 to the real grape weight observed in each vine after harvesting. |
Palavras-Chave: |
3-D phenotyping; 3D phenotyping; Estimativa de podução; Fenotipagem 3D; Métodos não-invasivos; Multiple view stereo; Non invasive methods; Non-invasive methods; Simultaneous localization and mapping; SLAM; Videira; Visão estéro múltipla; Yeld estimation; Yield estimation. |
Thesagro: |
Viticultura. |
Thesaurus Nal: |
Phenotype; viticulture. |
Categoria do assunto: |
-- X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/169609/1/Automatic-grape-SBIAgro.pdf
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/171151/1/P-Automatic-grape-bunch....pdf
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Marc: |
LEADER 02004nam a2200385 a 4500 001 2083291 005 2020-01-21 008 2017 bl uuuu u00u1 u #d 020 $a978-85-85783-75-4 100 1 $aSANTOS, T. T. 245 $aAutomatic grape bunch detection in vineyards based on affordable 3D phenotyping using a consumer webcam.$h[electronic resource] 260 $aIn: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 11., 2017, Campinas. Ciência de dados na era da agricultura digital: anais. Campinas: Editora da Unicamp: Embrapa Informática Agropecuária$c2017 300 $ap. 89-98. 500 $aSBIAgro 2017. 520 $aThis work presents a methodology for 3-D phenotyping of vineyards based on images captured by a low cost high-definition webcamera. A novel software application integrated visual odometry and multiple-view stereo components to create dense and accurate three-dimensional points clouds for vines, properly transformed to millimeter scale. Geometrical and color features of the points were employed by a classification procedure that reached 93% of accuracy on detecting points belonging to grapes. Individual bunches were automatically delimited and their volumes estimated. The sum of the estimated volumes per vine presented a coefficient of correlation of R = 0.99 to the real grape weight observed in each vine after harvesting. 650 $aPhenotype 650 $aviticulture 650 $aViticultura 653 $a3-D phenotyping 653 $a3D phenotyping 653 $aEstimativa de podução 653 $aFenotipagem 3D 653 $aMétodos não-invasivos 653 $aMultiple view stereo 653 $aNon invasive methods 653 $aNon-invasive methods 653 $aSimultaneous localization and mapping 653 $aSLAM 653 $aVideira 653 $aVisão estéro múltipla 653 $aYeld estimation 653 $aYield estimation 700 1 $aBASSOI, L. H. 700 1 $aOLDONI, H. 700 1 $aMARTINS, R. L.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |