CLASSIFICATION OF THE INITIAL DEVELOPMENT OF EUCALIPTUS USING DATA MINING TECHNIQUES

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Data

2017-04-01

Autores

Lima, Elizeu de Souza
Souza, Zigomar Menezes de
Montanari, Rafael [UNESP]
Medeiros Oliveira, Stanley Robson de
Lovera, Lenon Henrique
Vieira Farhate, Camila Viana

Título da Revista

ISSN da Revista

Título de Volume

Editor

Univ Federal Lavras-ufla

Resumo

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 (X-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.

Descrição

Palavras-chave

Eucalyptus urograndis, Individual wood volume, Feature selection, Entisol quartzipsamment soil, Decision tree

Como citar

Cerne. Lavras: Univ Federal Lavras-ufla, v. 23, n. 2, p. 201-208, 2017.

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