CLASSIFICATION OF THE INITIAL DEVELOPMENT OF EUCALIPTUS USING DATA MINING TECHNIQUES
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| Format: | Artículo científico |
| Sprache: | en |
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Universidade Federal de Lavras
2017
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| _version_ | 1876434191837560832 |
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| author | Elizeu de Souza Lima |
| author_facet | Elizeu de Souza Lima |
| contents | CLASSIFICATION OF THE INITIAL DEVELOPMENT OF EUCALIPTUS USING DATA MINING TECHNIQUES Elizeu de Souza Lima Zigomar Menezes de Souza Rafael Montanari Stanley Robson de Medeiros Oliveira Lenon Henrique Lovera Camila Viana Vieira Farhate Agrociencias Decision tree Feature selection Eucalyptus urograndis Individual wood volume Entisol quartzipsamment soil 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 decision- making process. The aim of this study was to model the influence of climate and physico- chemical 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, Correlation- based Feature Selection (CFS), Chi-square test ( χ 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. 2017 artículo científico 0104-7760 https://www.redalyc.org/articulo.oa?id=74451402007 en http://www.redalyc.org/revista.oa?id=744 CERNE application/pdf Universidade Federal de Lavras CERNE (Brasil) Num.2 Vol.23 |
| format | Artículo científico |
| id | redalyc_74451402007 |
| institution | Redalyc |
| language | en |
| publishDate | 2017 |
| publisher | Universidade Federal de Lavras |
| spellingShingle | CLASSIFICATION OF THE INITIAL DEVELOPMENT OF EUCALIPTUS USING DATA MINING TECHNIQUES Elizeu de Souza Lima Agrociencias Decision tree Feature selection Eucalyptus urograndis Individual wood volume Entisol quartzipsamment soil CLASSIFICATION OF THE INITIAL DEVELOPMENT OF EUCALIPTUS USING DATA MINING TECHNIQUES Elizeu de Souza Lima Zigomar Menezes de Souza Rafael Montanari Stanley Robson de Medeiros Oliveira Lenon Henrique Lovera Camila Viana Vieira Farhate Agrociencias Decision tree Feature selection Eucalyptus urograndis Individual wood volume Entisol quartzipsamment soil 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 decision- making process. The aim of this study was to model the influence of climate and physico- chemical 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, Correlation- based Feature Selection (CFS), Chi-square test ( χ 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. 2017 artículo científico 0104-7760 https://www.redalyc.org/articulo.oa?id=74451402007 en http://www.redalyc.org/revista.oa?id=744 CERNE application/pdf Universidade Federal de Lavras CERNE (Brasil) Num.2 Vol.23 |
| title | CLASSIFICATION OF THE INITIAL DEVELOPMENT OF EUCALIPTUS USING DATA MINING TECHNIQUES |
| topic | Agrociencias Decision tree Feature selection Eucalyptus urograndis Individual wood volume Entisol quartzipsamment soil |
| url | https://www.redalyc.org/articulo.oa?id=74451402007 |