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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
07/07/2017 |
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Data da última atualização: |
28/02/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LIMA, E. de S.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; LOVERA, L. H.; FARHATE, C. V. V. |
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Afiliação: |
ELIZEU DE SOUZA LIMA, Unicamp; ZIGOMAR MENEZES DE SOUZA, USP, Jaboticabal; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; LENON HENRIQUE LOVERA, Unicamp; CAMILA VIANA VIEIRA FARHATE, Unicamp. |
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Título: |
Classification of the initial development of eucaliptus using data mining techniques. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Cerne, v. 23, n. 2, p. 201-208, 2017. |
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DOI: |
10.1590/01047760201723022296 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract - Eucalyptus plantation has expanded considerably in Brazil, especially in regions where soils have low fertility, such as in Brazilian Cerrados. To achieve greater productivity, it is essential to know the needs of the soil and the right moment to correct it. Mathematical and computational models have been used as a promising alternative to help in this decisionmaking process. The aim of this study was to model the influence of climate and physicochemical attributes in the development of Eucalyptus urograndis in Entisol quartzipsamment soil using the decision tree induction technique. To do so, we used 30 attributes, 29 of them are predictive and one is the target-attribute or response variable regarding the height of the eucalyptus. We defined four approaches to select these features: no selection, Correlationbased Feature Selection (CFS), Chi-square test (?2) and Wrapper. To classify the data, we used the decision tree induction technique available in the Weka software 3.6. This data mining technique allowed us to create a classification model for the initial development of eucalyptus. From this model, one can predict new cases in different production classes, in which the individual wood volume (IWV) and the diameter at breast height (DBH) are crucial features to predict the growth of Eucalyptus urograndis, in addition to the presence of chemical soil components such as: magnesium (Mg+2), phosphorus (P), aluminum (Al+3), potassium (K+), potential acidity (H + Al), hydrogen potential (pH), and physical attributes such as soil resistance to penetration and related to climate, such as minimum temperature. MenosAbstract - Eucalyptus plantation has expanded considerably in Brazil, especially in regions where soils have low fertility, such as in Brazilian Cerrados. To achieve greater productivity, it is essential to know the needs of the soil and the right moment to correct it. Mathematical and computational models have been used as a promising alternative to help in this decisionmaking process. The aim of this study was to model the influence of climate and physicochemical attributes in the development of Eucalyptus urograndis in Entisol quartzipsamment soil using the decision tree induction technique. To do so, we used 30 attributes, 29 of them are predictive and one is the target-attribute or response variable regarding the height of the eucalyptus. We defined four approaches to select these features: no selection, Correlationbased Feature Selection (CFS), Chi-square test (?2) and Wrapper. To classify the data, we used the decision tree induction technique available in the Weka software 3.6. This data mining technique allowed us to create a classification model for the initial development of eucalyptus. From this model, one can predict new cases in different production classes, in which the individual wood volume (IWV) and the diameter at breast height (DBH) are crucial features to predict the growth of Eucalyptus urograndis, in addition to the presence of chemical soil components such as: magnesium (Mg+2), phosphorus (P), aluminum (Al+3), potassium (K+), potential acidity (H + Al... Mostrar Tudo |
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Palavras-Chave: |
Mineração de dados. |
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Thesagro: |
Eucalipto. |
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Thesaurus Nal: |
Eucalyptus; Technology. |
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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/161528/1/classificacao-desenv.pdf
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Marc: |
LEADER 02313naa a2200229 a 4500
001 2072262
005 2018-02-28
008 2017 bl uuuu u00u1 u #d
024 7 $a10.1590/01047760201723022296$2DOI
100 1 $aLIMA, E. de S.
245 $aClassification of the initial development of eucaliptus using data mining techniques.$h[electronic resource]
260 $c2017
520 $aAbstract - Eucalyptus plantation has expanded considerably in Brazil, especially in regions where soils have low fertility, such as in Brazilian Cerrados. To achieve greater productivity, it is essential to know the needs of the soil and the right moment to correct it. Mathematical and computational models have been used as a promising alternative to help in this decisionmaking process. The aim of this study was to model the influence of climate and physicochemical attributes in the development of Eucalyptus urograndis in Entisol quartzipsamment soil using the decision tree induction technique. To do so, we used 30 attributes, 29 of them are predictive and one is the target-attribute or response variable regarding the height of the eucalyptus. We defined four approaches to select these features: no selection, Correlationbased Feature Selection (CFS), Chi-square test (?2) and Wrapper. To classify the data, we used the decision tree induction technique available in the Weka software 3.6. This data mining technique allowed us to create a classification model for the initial development of eucalyptus. From this model, one can predict new cases in different production classes, in which the individual wood volume (IWV) and the diameter at breast height (DBH) are crucial features to predict the growth of Eucalyptus urograndis, in addition to the presence of chemical soil components such as: magnesium (Mg+2), phosphorus (P), aluminum (Al+3), potassium (K+), potential acidity (H + Al), hydrogen potential (pH), and physical attributes such as soil resistance to penetration and related to climate, such as minimum temperature.
650 $aEucalyptus
650 $aTechnology
650 $aEucalipto
653 $aMineração de dados
700 1 $aSOUZA, Z. M. de
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aLOVERA, L. H.
700 1 $aFARHATE, C. V. V.
773 $tCerne$gv. 23, n. 2, p. 201-208, 2017.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 10 | |
| 3. |  | FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; CARVALHO, J. L. N.; LA SCALA JÚNIOR, N.; SANTOS, A. P. G. Classification of soil respiration in areas of sugarcane renewal using decision tree. Scientia Agricola, v. 75, n. 3, p. 216-224, May/June 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 5. | | TAVARES, R. L. M.; OLIVEIRA, S. R. de M.; BARROS, F. M. M. de; FARHATE, C. V. V.; SOUZA, Z. M. de; LA SCALA JUNIOR, N. Prediction of soil CO2 flux in sugarcane management systems using the Random Forest approach. Scientia Agricola, Piracicaba, v. 74, n. 4, p. 281-287, July/Aug. 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 6. | | MARÇAL, M. F. M.; SOUZA, Z. M. de; TAVARES, R. L. M.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M.; GALINDO, F. S. Predictive models to estimate carbon stocks in agroforestry systems. Forests, v. 12, n. 9, p. 1-15, Sept. 2021. Article 1240. Na publicação: Stanley Robson Medeiros Oliveira.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 7. | | FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M.; LOVERA, L. H.; OLIVEIRA, I. N. de; GUIMARÃES, E. M. Data mining techniques for classification of soil CO2 emission. In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: abstracts. Viçosa, MG: SBCS, 2018. Não paginado. WCSS 2018.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 8. | | LÓPEZ-NORONHA, R.; SOUZA, Z. M. de; SOARES, M. D. R.; CAMPOS, M. C. C.; FARHATE, C. V. V.; OLIVEIRA, S. R. de M. Soil carbon stock in archaeological black earth under different land use systems in the Brazilian Amazon. Agronomy Journal, v. 112, n. 5, p. 4437-4450, Sept./Oct. 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 9. | | NORONHA, R. L.; SOARES, M. D. R.; OLIVEIRA, I. N. de; FARHATE, C. V. V.; SOUZA, Z. M. de; OLIVEIRA, S. R. de M. Soil carbon stock predictive models on archaeological black lands - natural and transformed. In: WORLD CONGRESS OF SOIL SCIENCE, 21., 2018, Rio de Janeiro. Soil science: beyond food and fuel: abstracts. Viçosa, MG: SBCS, 2018. Não paginado. WCSS 2018.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 10 | |
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