| |
|
|
| Registros recuperados : 2 | |
| 2. |  | REIS, M. G. dos; RIBEIRO, A.; BAESSO, R. C. E.; SOUZA, W. G. de; FONSECA, S.; LOOS, R. A. Balanço hídrico e de energia para plantios de eucalipto com cobertura parcial do solo. Ciência Florestal, Santa Maria, RS, v. 24, n. 1, p. 117-126, jan./mar. 2014.| Biblioteca(s): Epagri-Sede. |
|   |
| Registros recuperados : 2 | |
|
|
|
Registro Completo
|
Biblioteca(s): |
Epagri-Sede. |
|
Data corrente: |
21/11/2018 |
|
Data da última atualização: |
21/11/2018 |
|
Tipo da produção científica: |
Resumo em Anais de Congresso |
|
Autoria: |
SILVA, E. B.; GIASSON, É.; DOTTO, A. C.; CATEN, A. T.; DEMATTÊ, J. A. M.; VEIGA, M. |
|
Título: |
VIS-NIR SPECTROSCOPY FOR PREDICTING SAND, SILT AND CLAY CONTENTS USING LEGACY SOIL SAMPLES OF SANTA CATARINA STATE. |
|
Ano de publicação: |
2017 |
|
Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 36., 2017, Belém. Resumos... Viçosa: Sociedade Brasileira de Ciência do Solo, 2017. |
|
Idioma: |
Inglês |
|
Conteúdo: |
The quantitative distribution of particle size is one of the most important soil
properties. Diffuse reflectance spectroscopy in the visible and near infrared (Vis-NIR) region
have been employed to characterize and quantify soil properties in a fast, non-destructive and
low cost manner. The objective of this study was to develop a calibration model with
laboratory-based soil Vis-NIR spectra for sand, silt and clay content using multivariate
analysis techniques. Materials and Methods - A set of 1534 legacy soil samples
representative of the crops from 260 municipalities (80% of total) of Santa Catarina state were
used. Sand, silt and clay content (in percentage) were determined in the laboratory by the
pipette method. Soil spectra was obtained using the FieldSpec 3 (ASD) sensor with a range of
370 to 2500 nm (Vis-NIR). Four multivariate techniques (Partial least-squares - PLS, Support
vector machine - SVM, Random forest - RF, Gaussian process regression - GPR) and eight
pre-processing transformations (including raw spectra, smoothing, derivatives, normalization
and non-linear transformations) of spectral data were compared. The coefficient of
determination (R 2 ) and the root mean square error (RMSE) were used to evaluate the models.
All analyses and modeling were performed using the AlradSpectra package in R. Results and
Discussion - On average, the predictive performance of the multivariate techniques decreased
in the following order: PLS>RF>GPR>SVM for sand; RF>PLS>GPR>SVM for silt; and
PLS>RF>GPR>SVM for clay. The combinations of pre-processing transformation and
multivariate technique that achieved the best predictive result was Savitzky-Golay first
derivative/RF (SGD1-RF) for sand (R 2 v=0.67, RMSEv=11.56%); SGD1-RF for silt (R
2 v=0.43, RMSEv=8.51%); and Normalization/SVM for clay (R 2 v=0.76, RMSEv=8.68%). Conclusions
- This study offers the possibility to perform different modeling strategies using legacy data in
order to predict sand, silt and clay content, by Vis-NIR spectroscopy technique. On the other
hand, silt was not predictable with the same effectiveness and more studies should be
conducted. MenosThe quantitative distribution of particle size is one of the most important soil
properties. Diffuse reflectance spectroscopy in the visible and near infrared (Vis-NIR) region
have been employed to characterize and quantify soil properties in a fast, non-destructive and
low cost manner. The objective of this study was to develop a calibration model with
laboratory-based soil Vis-NIR spectra for sand, silt and clay content using multivariate
analysis techniques. Materials and Methods - A set of 1534 legacy soil samples
representative of the crops from 260 municipalities (80% of total) of Santa Catarina state were
used. Sand, silt and clay content (in percentage) were determined in the laboratory by the
pipette method. Soil spectra was obtained using the FieldSpec 3 (ASD) sensor with a range of
370 to 2500 nm (Vis-NIR). Four multivariate techniques (Partial least-squares - PLS, Support
vector machine - SVM, Random forest - RF, Gaussian process regression - GPR) and eight
pre-processing transformations (including raw spectra, smoothing, derivatives, normalization
and non-linear transformations) of spectral data were compared. The coefficient of
determination (R 2 ) and the root mean square error (RMSE) were used to evaluate the models.
All analyses and modeling were performed using the AlradSpectra package in R. Results and
Discussion - On average, the predictive performance of the multivariate techniques decreased
in the following orde... Mostrar Tudo |
|
Palavras-Chave: |
chemometrics modeling; Diffuse reflectance spectroscopy; spectral. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
|
|
|
Marc: |
LEADER 02985naa a2200217 a 4500
001 1127954
005 2018-11-21
008 2017 bl uuuu u00u1 u #d
100 1 $aSILVA, E. B.
245 $aVIS-NIR SPECTROSCOPY FOR PREDICTING SAND, SILT AND CLAY CONTENTS USING LEGACY SOIL SAMPLES OF SANTA CATARINA STATE.$h[electronic resource]
260 $c2017
520 $aThe quantitative distribution of particle size is one of the most important soil properties. Diffuse reflectance spectroscopy in the visible and near infrared (Vis-NIR) region have been employed to characterize and quantify soil properties in a fast, non-destructive and low cost manner. The objective of this study was to develop a calibration model with laboratory-based soil Vis-NIR spectra for sand, silt and clay content using multivariate analysis techniques. Materials and Methods - A set of 1534 legacy soil samples representative of the crops from 260 municipalities (80% of total) of Santa Catarina state were used. Sand, silt and clay content (in percentage) were determined in the laboratory by the pipette method. Soil spectra was obtained using the FieldSpec 3 (ASD) sensor with a range of 370 to 2500 nm (Vis-NIR). Four multivariate techniques (Partial least-squares - PLS, Support vector machine - SVM, Random forest - RF, Gaussian process regression - GPR) and eight pre-processing transformations (including raw spectra, smoothing, derivatives, normalization and non-linear transformations) of spectral data were compared. The coefficient of determination (R 2 ) and the root mean square error (RMSE) were used to evaluate the models. All analyses and modeling were performed using the AlradSpectra package in R. Results and Discussion - On average, the predictive performance of the multivariate techniques decreased in the following order: PLS>RF>GPR>SVM for sand; RF>PLS>GPR>SVM for silt; and PLS>RF>GPR>SVM for clay. The combinations of pre-processing transformation and multivariate technique that achieved the best predictive result was Savitzky-Golay first derivative/RF (SGD1-RF) for sand (R 2 v=0.67, RMSEv=11.56%); SGD1-RF for silt (R 2 v=0.43, RMSEv=8.51%); and Normalization/SVM for clay (R 2 v=0.76, RMSEv=8.68%). Conclusions - This study offers the possibility to perform different modeling strategies using legacy data in order to predict sand, silt and clay content, by Vis-NIR spectroscopy technique. On the other hand, silt was not predictable with the same effectiveness and more studies should be conducted.
653 $achemometrics modeling
653 $aDiffuse reflectance spectroscopy
653 $aspectral
700 1 $aGIASSON, É.
700 1 $aDOTTO, A. C.
700 1 $aCATEN, A. T.
700 1 $aDEMATTÊ, J. A. M.
700 1 $aVEIGA, M.
773 $tIn: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 36., 2017, Belém. Resumos... Viçosa: Sociedade Brasileira de Ciência do Solo, 2017.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Epagri-Sede (Epagri-Sede) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Expressão de busca inválida. Verifique!!! |
|
|
