Data/hora: 03/06/2024 10:11:06

Provedor de dados: 	63	
País: 	Brazil	
Título: 	Biometric characteristics and canopy reflectance association for early-stage sugarcane biomass prediction	
Autores: 	Rocha,Murillo Grespan da; Barros,Flávio Margarito Martins de; Oliveira,Stanley Robson de Medeiros; Amaral,Lucas Rios do.	
Data: 	2019-07-01	
Ano: 	2019	
Palavras-chave: 	Random forest; Canopy sensor; Vegetation indices; Precision farming; Data mining.	
Resumo: 	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.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162019001400274	
Editor: 	São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"	
Relação: 	10.1590/1678-992x-2017-0301	
Formato: 	text/html	
Fonte: 	Scientia Agricola  v.76 n.4 2019	
Fonte: 	info:eu-repo/semantics/openAccess