Predictive models of chlorophyll content in sugarcane seedlings using spectral images

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dc.creatorNelson Felipe Oliveros Mesa
dc.creatorRodolpho Cesar dos Reis Tinini
dc.creatorDaniel dos Santos Costa
dc.creatorRodrigo Pereira Ramos
dc.creatorCaio Bruno Wetterich
dc.creatorBarbara Janet Teruel Mederos
dc.date.accessioned2023-04-24T10:50:27Z
dc.date.accessioned2025-09-09T00:55:43Z
dc.date.available2023-04-24T10:50:27Z
dc.date.issued2021
dc.identifier.doihttps://doi.org/10.1590/1809-4430-Eng.Agric.v41n4p475-484/2021
dc.identifier.issn1809-4430
dc.identifier.urihttps://hdl.handle.net/1843/52374
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofEngenharia Agrícola
dc.rightsAcesso Aberto
dc.subjectCana-de-açúcar
dc.subjectClorofila
dc.subjectQuimiometria
dc.subjectImagens multiespectrais
dc.titlePredictive models of chlorophyll content in sugarcane seedlings using spectral images
dc.typeArtigo de periódico
local.citation.epage484
local.citation.issue4
local.citation.spage475
local.citation.volume41
local.description.resumoChlorophyll content is a widely used parameter for nutritional status diagnosis in sugarcane. This study aimed to develop a predictive model of chlorophyll content in sugarcane seedlings using spectral imagery analysis within the electromagnetic spectrum visible range. The experiment was carried out in a split-plot design, with two fertilization rates and three sugarcane cultivars. For chlorophyll analysis, 144 leaves were collected from seedlings. Chlorophyll contents were extracted and measured by SPAD-502 meter. Spectral images within the range of 480 to 710 nm were analyzed using reflectance, absorbance (white source), and fluorescence (source at 405 and 470 nm) responses. Predictive models were developed using multivariate regression methods such as Principal Component Regression and Partial Least Squares Regression. We chose the best model through absorbance response using variable selection and the PLSR method (R2P = 0.718 and RMSEP = 7.665). The wavelengths of 480, 490, 500, 600, 630, and 640 nm were identified as the best for total chlorophyll content determination. The spectral image processing-based method can provide a chlorophyll measurement equivalent to SPAD, with the advantage of having a higher spatial coverage over the entire leaf area. Besides, it can also support automation of the chlorophyll measurement in greenhouses.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.scielo.br/j/eagri/a/yGF7hFcxBnS8844gTGMQGgM/

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