Please use this identifier to cite or link to this item:
http://hdl.handle.net/1843/52374| Type: | Artigo de Periódico |
| Title: | Predictive models of chlorophyll content in sugarcane seedlings using spectral images |
| Authors: | Nelson Felipe Oliveros Mesa Rodolpho Cesar dos Reis Tinini Daniel dos Santos Costa Rodrigo Pereira Ramos Caio Bruno Wetterich Barbara Janet Teruel Mederos |
| Abstract: | Chlorophyll 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. |
| Subject: | Cana-de-açúcar Clorofila Quimiometria Imagens multiespectrais |
| language: | eng |
| metadata.dc.publisher.country: | Brasil |
| Publisher: | Universidade Federal de Minas Gerais |
| Publisher Initials: | UFMG |
| metadata.dc.publisher.department: | ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS |
| Rights: | Acesso Aberto |
| metadata.dc.identifier.doi: | https://doi.org/10.1590/1809-4430-Eng.Agric.v41n4p475-484/2021 |
| URI: | http://hdl.handle.net/1843/52374 |
| Issue Date: | 2021 |
| metadata.dc.url.externa: | https://www.scielo.br/j/eagri/a/yGF7hFcxBnS8844gTGMQGgM/ |
| metadata.dc.relation.ispartof: | Engenharia Agrícola |
| Appears in Collections: | Artigo de Periódico |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Predictive models of chlorophyll content in sugarcane seedlings using spectral images.pdf | 630.52 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.