Title:	Biometric characteristics and canopy reflectance association for early-stage sugarcane.
Authors:	ROCHA, M. G. da BARROS, F. M. M. de OLIVEIRA, S. R. de M. AMARAL, L. R. do
Affiliation:	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.
Date Issued:	2019
Citation:	Scientia Agricola, v. 76, n. 4, p. 274-280, July/Aug. 2019
Description:	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.
Thesagro:	Biomassa Cana de Açúcar Agricultura de Precisão
NAL Thesaurus:	Biomass Sugarcane Precision agriculture Vegetation index
Keywords:	Floresta aleatória Índice de vegetação Mineração de dados Precision farming Random forest Vegetation indices Data mining Canopy sensor
DOI:	http://dx.doi.org/10.1590/1678-992X-2017-0301
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPTIA)