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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/52374
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dc.creatorNelson Felipe Oliveros Mesapt_BR
dc.creatorRodolpho Cesar dos Reis Tininipt_BR
dc.creatorDaniel dos Santos Costapt_BR
dc.creatorRodrigo Pereira Ramospt_BR
dc.creatorCaio Bruno Wetterichpt_BR
dc.creatorBarbara Janet Teruel Mederospt_BR
dc.date.accessioned2023-04-24T10:50:27Z-
dc.date.available2023-04-24T10:50:27Z-
dc.date.issued2021-
dc.citation.volume41pt_BR
dc.citation.issue4pt_BR
dc.citation.spage475pt_BR
dc.citation.epage484pt_BR
dc.identifier.doihttps://doi.org/10.1590/1809-4430-Eng.Agric.v41n4p475-484/2021pt_BR
dc.identifier.issn1809-4430pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/52374-
dc.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.pt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofEngenharia Agrícola-
dc.rightsAcesso Abertopt_BR
dc.subject.otherCana-de-açúcarpt_BR
dc.subject.otherClorofilapt_BR
dc.subject.otherQuimiometriapt_BR
dc.subject.otherImagens multiespectraispt_BR
dc.titlePredictive models of chlorophyll content in sugarcane seedlings using spectral imagespt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.scielo.br/j/eagri/a/yGF7hFcxBnS8844gTGMQGgM/pt_BR
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