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Registro Completo |
Biblioteca(s): |
Embrapa Soja. |
Data corrente: |
17/12/2015 |
Data da última atualização: |
29/09/2017 |
Tipo da produção científica: |
Artigo de Divulgação na Mídia |
Autoria: |
SOARES, R. M. |
Afiliação: |
RAFAEL MOREIRA SOARES, CNPSO. |
Título: |
A influência do el niño na safra de soja. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
Informativo Meridional, Londrina, v. 15, n. 56, p. 7, dez. 2015. |
Idioma: |
Português |
Thesagro: |
Doença de planta; Doença fúngica; Ferrugem; Phakopsora Pachyrhizi; Soja. |
Thesaurus Nal: |
Plant diseases and disorders; Soybean rust; Soybeans. |
Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
Marc: |
LEADER 00549nam a2200193 a 4500 001 2031974 005 2017-09-29 008 2015 bl uuuu u00u1 u #d 100 1 $aSOARES, R. M. 245 $aA influência do el niño na safra de soja. 260 $aInformativo Meridional, Londrina, v. 15, n. 56, p. 7, dez. 2015.$c2015 650 $aPlant diseases and disorders 650 $aSoybean rust 650 $aSoybeans 650 $aDoença de planta 650 $aDoença fúngica 650 $aFerrugem 650 $aPhakopsora Pachyrhizi 650 $aSoja
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Biblioteca(s): |
Embrapa Unidades Centrais. |
Data corrente: |
07/03/2013 |
Data da última atualização: |
01/09/2017 |
Autoria: |
SARMENTO, E. C.; GIASSON, E.; WEBER, E.; FLORES, C. A.; HASENACK, H. |
Afiliação: |
ELIANA CASCO SARMENTO, UFRGS; ELVIO GIASSON, UFRGS; ELISEU WEBER, UFRGS; CARLOS ALBERTO FLORES, CPACT; HEINRICH HASENACK, UFRGS. |
Título: |
Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density. |
Ano de publicação: |
2012 |
Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 47, n. 9, p. 1395-1403, set. 2012. |
Páginas: |
p.1395-1403 |
Idioma: |
Inglês |
Notas: |
Título em português: Predição de ordens de solos com alta resolução espacial: resposta de diferentes classificadores à densidade de amostragem. |
Conteúdo: |
The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self-organizing map, SOM; and multi-layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha. |
Palavras-Chave: |
Appelation of origin; Decision tree; Digital elevation model; Neural Network. |
Thesaurus NAL: |
Geographic information systems; Soil surveys. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/78412/1/PAB-v.47-n.9-p.1395-1403.pdf
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Marc: |
LEADER 02200naa a2200265 a 4500 001 1952501 005 2017-09-01 008 2012 bl uuuu u00u1 u #d 100 1 $aSARMENTO, E. C. 245 $aPrediction of soil orders with high spatial resolution$bresponse of different classifiers to sampling density. 260 $c2012 300 $ap.1395-1403 500 $aTítulo em português: Predição de ordens de solos com alta resolução espacial: resposta de diferentes classificadores à densidade de amostragem. 520 $aThe objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self-organizing map, SOM; and multi-layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha. 650 $aGeographic information systems 650 $aSoil surveys 653 $aAppelation of origin 653 $aDecision tree 653 $aDigital elevation model 653 $aNeural Network 700 1 $aGIASSON, E. 700 1 $aWEBER, E. 700 1 $aFLORES, C. A. 700 1 $aHASENACK, H. 773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 47, n. 9, p. 1395-1403, set. 2012.
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