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 | Acesso ao texto completo restrito à biblioteca da Embrapa Trigo. Para informações adicionais entre em contato com cnpt.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
29/01/2020 |
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Data da última atualização: |
17/09/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SANTOS, M. R. dos; MADALOZZO, G. A.; FERNANDES, J. M. C.; RIEDER, R. |
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Afiliação: |
Marcos Roberto dos Santos, Universidade de Passo Fundo, Rio Grande do Sul, Brazil; Guilherme Afonso Madalozzo, Universidade de Passo Fundo, Rio Grande do Sul, Brazil; JOSE MAURICIO CUNHA FERNANDES, CNPT; Rafael Rieder, Universidade de Passo Fundo, Rio Grande do Sul, Brazil. |
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Título: |
Fenômica: a computer vision system for high-throughput phenotyping. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
International Journal of Agricultural and Environmental Information Systems (IJAEIS), v. 11, n. 1, p. 1-22, Online, 2020. |
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DOI: |
10.4018/IJAEIS.2020010101 |
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Idioma: |
Inglês |
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Conteúdo: |
Computer vision and image processing procedures could obtain crop data frequently and precisely, such as vegetation indexes, and correlating them with other variables, like biomass and crop yield. This work presents the development of a computer vision system for high-throughput phenotyping, considering three solutions: an image capture software linked to a low-cost appliance; an image-processing program for feature extraction; and a web application for results' presentation. As a case study, we used normalized difference vegetation index (NDVI) data from a wheat crop experiment of the Brazilian Agricultural Research Corporation. Regression analysis showed that NDVI explains 98.9, 92.8, and 88.2% of the variability found in the biomass values for crop plots with 82, 150, and 200 kg of N ha1 fertilizer applications, respectively. As a result, NDVI generated by our system presented a strong correlation with the biomass, showing a way to specify a new yield prediction model from the beginning of the crop. |
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Palavras-Chave: |
Computer vision system; High-throughput phenotyping. |
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Thesaurus Nal: |
Biomass; Crop yield. |
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Categoria do assunto: |
-- |
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Marc: |
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001 2119507
005 2020-09-17
008 2020 bl uuuu u00u1 u #d
024 7 $a10.4018/IJAEIS.2020010101$2DOI
100 1 $aSANTOS, M. R. dos
245 $aFenômica$ba computer vision system for high-throughput phenotyping.$h[electronic resource]
260 $c2020
520 $aComputer vision and image processing procedures could obtain crop data frequently and precisely, such as vegetation indexes, and correlating them with other variables, like biomass and crop yield. This work presents the development of a computer vision system for high-throughput phenotyping, considering three solutions: an image capture software linked to a low-cost appliance; an image-processing program for feature extraction; and a web application for results' presentation. As a case study, we used normalized difference vegetation index (NDVI) data from a wheat crop experiment of the Brazilian Agricultural Research Corporation. Regression analysis showed that NDVI explains 98.9, 92.8, and 88.2% of the variability found in the biomass values for crop plots with 82, 150, and 200 kg of N ha1 fertilizer applications, respectively. As a result, NDVI generated by our system presented a strong correlation with the biomass, showing a way to specify a new yield prediction model from the beginning of the crop.
650 $aBiomass
650 $aCrop yield
653 $aComputer vision system
653 $aHigh-throughput phenotyping
700 1 $aMADALOZZO, G. A.
700 1 $aFERNANDES, J. M. C.
700 1 $aRIEDER, R.
773 $tInternational Journal of Agricultural and Environmental Information Systems (IJAEIS)$gv. 11, n. 1, p. 1-22, Online, 2020.
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