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Type: Artigo
Title: From Spreadsheets To Sugar Content Modeling: A Data Mining Approach
Author: Gravina de Oliveira
Monique Pires; Bocca
Felipe Ferreira; Antunes Rodrigues
Luiz Henrique
Abstract: Sugarcane mills need sugar content estimates in advance to establish their commercial strategy. To obtain these estimates, mills rely on historical averages or maturation curves. Crop models have also been developed to provide those estimates. Leveraging mill data about fields and sugar content at harvest, we developed empirical models using different data Mining techniques along with the RReliefF algorithm for feature selection. The best model was attained with Random Forest with features selected by RReliefF, having a mean absolute error of 2.02 kg Mg-1. This model outperformed Support Vector Regression And Regression Trees with and without feature selection. Models were also evaluated by the Regression Error Characteristic Curves, which showed that the best model was able to predict 90% of the observations within a precision of 5.40 kg Mg-1. (C) 2016 Elsevier B.V. All rights reserved.
Subject: Sugarcane Ripening
Machine Learning
Empirical Modeling
Total Recoverable Sugar
Crop Modeling
Editor: Elsevier Sci LTD
Rights: fechado
Identifier DOI: 10.1016/j.compag.2016.11.012
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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