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Biometric characteristics and canopy reflectance association for early-stage sugarcane. Repositório Alice
ROCHA, M. G. da; BARROS, F. M. M. de; OLIVEIRA, S. R. de M.; AMARAL, L. R. do.
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...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Floresta aleatória; Índice de vegetação; Mineração de dados; Precision farming; Random forest; Vegetation indices; Data mining; Canopy sensor; Biomassa; Cana de Açúcar; Agricultura de Precisão; Biomass; Sugarcane; Precision agriculture; Vegetation index.
Ano: 2019 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1110823
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Biometric characteristics and canopy reflectance association for early-stage sugarcane biomass prediction Scientia Agricola
Rocha,Murillo Grespan da; Barros,Flávio Margarito Martins de; Oliveira,Stanley Robson de Medeiros; Amaral,Lucas Rios do.
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...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Random forest; Canopy sensor; Vegetation indices; Precision farming; Data mining.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001400274
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