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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
24/07/2019 |
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Data da última atualização: |
02/10/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ROCHA, M. G. da; BARROS, F. M. M. de; OLIVEIRA, S. R. de M.; AMARAL, L. R. do. |
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Afiliação: |
MURILLO GRESPAN DA ROCHA, Feagri/Unicamp; FLÁVIO MARGARITO MARTINS DE BARROS, Feagri/Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; LUCAS RIOS DO AMARAL, Feagri/Unicamp. |
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Título: |
Biometric characteristics and canopy reflectance association for early-stage sugarcane. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Scientia Agricola, v. 76, n. 4, p. 274-280, July/Aug. 2019 |
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DOI: |
http://dx.doi.org/10.1590/1678-992X-2017-0301 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT: Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple Linear Regression models are similarly able to predict biomass, and that associating biometric variables such as number of stalks and plant height with reflectance data can assist model performance, depending on the attributes selected. This indicates that, when estimating biomass in the early stages, sugarcane growers can carry out site-specific management in order to increase yield and reduce the use of inputs. |
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Palavras-Chave: |
Canopy sensor; Data mining; Floresta aleatória; Índice de vegetação; Mineração de dados; Precision farming; Random forest; Vegetation indices. |
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Thesagro: |
Agricultura de Precisão; Biomassa; Cana de Açúcar. |
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Thesaurus Nal: |
Biomass; Precision agriculture; Sugarcane; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199820/1/AP-Biometric-characteristics.pdf
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Marc: |
LEADER 02240naa a2200349 a 4500
001 2110823
005 2019-10-02
008 2019 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1590/1678-992X-2017-0301$2DOI
100 1 $aROCHA, M. G. da
245 $aBiometric characteristics and canopy reflectance association for early-stage sugarcane.$h[electronic resource]
260 $c2019
520 $aABSTRACT: Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple Linear Regression models are similarly able to predict biomass, and that associating biometric variables such as number of stalks and plant height with reflectance data can assist model performance, depending on the attributes selected. This indicates that, when estimating biomass in the early stages, sugarcane growers can carry out site-specific management in order to increase yield and reduce the use of inputs.
650 $aBiomass
650 $aPrecision agriculture
650 $aSugarcane
650 $aVegetation index
650 $aAgricultura de Precisão
650 $aBiomassa
650 $aCana de Açúcar
653 $aCanopy sensor
653 $aData mining
653 $aFloresta aleatória
653 $aÍndice de vegetação
653 $aMineração de dados
653 $aPrecision farming
653 $aRandom forest
653 $aVegetation indices
700 1 $aBARROS, F. M. M. de
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aAMARAL, L. R. do
773 $tScientia Agricola$gv. 76, n. 4, p. 274-280, July/Aug. 2019
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