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| 82. |  | TAVARES, S. C. C. de H.; COELHO, R. S. B. Fontes de resistencia em feijoeiro a racas de Colletotrichum lindemuthianum pertencentes aos grupos alfa, brasileiro I, mexicano I e delta encontradas no estado de Pernambuco. Fitopatologia Brasileira, Brasília, DF, v. 13, n. 2, p. 136, jul. 1988.| Biblioteca(s): Embrapa Semiárido. |
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| Registros recuperados : 444 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
10/01/2020 |
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Data da última atualização: |
16/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
VALADARES, A. P.; COELHO, R. M.; OLIVEIRA, S. R. de M. |
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Afiliação: |
ALAN PESSOA VALADARES, IAC; RICARDO MARQUES COELHO, IAC; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA. |
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Título: |
Preprocessing procedures and supervised classification applied to a database of systematic soil survey. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Scientia Agricola, v. 76, n. 5, p. 439-447, Sept./Oct. 2019. |
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DOI: |
http://dx.doi.org/10.1590/1678-992X-2017-0171 |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT:Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, ?Dois Córregos? (?Brotas? 1:100,000-scale sheet), ?São Pedro? and ?Laras? (?Piracicaba? 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the semi-detailed soil survey of the state of São Paulo, from the Brazilian Institute of Geography and Statistics (IBGE) 1:50,000-scale topographic sheets and from the 1:750,000-scale geological map of the state of São Paulo. The target variable was a soil map unit of four types: local ?soil unit? name and soil class at three hierarchical levels of the Brazilian System of Soil Classification (SiBCS). Different data preprocessing treatments and four algorithms all having different approaches were also tested. Results showed that composite soil map units were not adequate for the machine learning process. Class balance did not contribute to improving the performance of classifiers. Accuracy values of 78 % and a Kappa index of 0.67 were obtained after preprocessing procedures with Random Forest, the algorithm that performed best. Information from conventional map units of semi-detailed (4th order) 1:100,000 soil survey generated models with values for accuracy, precision, sensitivity, specificity and Kappa indexes that support their use in programs for systematic soil surveying. MenosABSTRACT:Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, ?Dois Córregos? (?Brotas? 1:100,000-scale sheet), ?São Pedro? and ?Laras? (?Piracicaba? 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the semi-detailed soil survey of the state of São Paulo, from the Brazilian Institute of Geography and Statistics (IBGE) 1:50,000-scale topographic sheets and from the 1:750,000-scale geological map of the state of São Paulo. The target variable was a soil map unit of four types: local ?soil unit? name and soil class at three hierarchical levels of the Brazilian System of Soil Classification (SiBCS). Different data preprocessing treatments and four algorithms all having different approaches were also tested. Results showed that composite soil map units were not adequate for the machine learning process. Class balance did not contribute to improving the performance of classifiers. Accuracy values of 78 % and a Kappa index of 0.6... Mostrar Tudo |
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Palavras-Chave: |
Aprendizado de máquina; Classificação de solos; Digital soil mapping; Machine learning algorithms; Pré-processamento; Random forest; Tacit soil-landscape relationships. |
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Thesagro: |
Solo. |
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Thesaurus NAL: |
Soil; Soil classification. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/208531/1/AP-Preprocessing-procedures.pdf
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Marc: |
LEADER 02762naa a2200277 a 4500
001 2118563
005 2020-01-16
008 2019 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1590/1678-992X-2017-0171$2DOI
100 1 $aVALADARES, A. P.
245 $aPreprocessing procedures and supervised classification applied to a database of systematic soil survey.$h[electronic resource]
260 $c2019
520 $aABSTRACT:Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, ?Dois Córregos? (?Brotas? 1:100,000-scale sheet), ?São Pedro? and ?Laras? (?Piracicaba? 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the semi-detailed soil survey of the state of São Paulo, from the Brazilian Institute of Geography and Statistics (IBGE) 1:50,000-scale topographic sheets and from the 1:750,000-scale geological map of the state of São Paulo. The target variable was a soil map unit of four types: local ?soil unit? name and soil class at three hierarchical levels of the Brazilian System of Soil Classification (SiBCS). Different data preprocessing treatments and four algorithms all having different approaches were also tested. Results showed that composite soil map units were not adequate for the machine learning process. Class balance did not contribute to improving the performance of classifiers. Accuracy values of 78 % and a Kappa index of 0.67 were obtained after preprocessing procedures with Random Forest, the algorithm that performed best. Information from conventional map units of semi-detailed (4th order) 1:100,000 soil survey generated models with values for accuracy, precision, sensitivity, specificity and Kappa indexes that support their use in programs for systematic soil surveying.
650 $aSoil
650 $aSoil classification
650 $aSolo
653 $aAprendizado de máquina
653 $aClassificação de solos
653 $aDigital soil mapping
653 $aMachine learning algorithms
653 $aPré-processamento
653 $aRandom forest
653 $aTacit soil-landscape relationships
700 1 $aCOELHO, R. M.
700 1 $aOLIVEIRA, S. R. de M.
773 $tScientia Agricola$gv. 76, n. 5, p. 439-447, Sept./Oct. 2019.
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Embrapa Agricultura Digital (CNPTIA) |
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