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Lack of Data: Is It Enough Estimating the Coffee Rust with Meteorological Time Series?

  • David Camilo CorralesEmail author
  • German Gutierrez
  • Jhonn Pablo RodriguezEmail author
  • Agapito Ledezma
  • Juan Carlos CorralesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10405)

Abstract

Rust is the most economically important coffee disease in the world. Coffee rust epidemics have affected a number of countries including: Colombia, Brazil and Central America. Researchers try to predict the Incidence Rate of Rust (IRR) through supervised learning models, nevertheless the available IRR measurements are few, then the data set does not represent a sample trustworthy of the population. In this paper we use Cubic Spline Interpolation algorithm to increase the measurements of Incidence Rate of Rust and subsequently we construct different subsets of meteorological time series: (i) Daily meteorology, (ii) Meteorological variation, and (iii) Previous meteorology using M5 Regression Tree, Support Vector Regression and Multi-Layer Perceptron. Previous meteorology with Multi-Layer Perceptron have shown better results in measures as Pearson Coefficient Correlation of 0.81 and Mean Absolute Error \(=\) 7.41%.

Keywords

Coffee rust Incidence rate of rust Regression models Time series Interpolation 

Notes

Acknowledgments

We thank Centro Nacional de Investigaciones de Café (Cenicafé) and Mr. Alvaro Gaitan Bustamante, PhD, for his knowledge. We also thank the Telematics Engineering Group (GIT) of the University of Cauca, and the Control Learning and Systems Optimization Group (CAOS) of the Carlos III University of Madrid, for the technical support. Finally, this work has been partially supported by AgroCloud project of the RICCLISA Program, and the Spanish Government (under projects TRA2011-29454-C03-03 and TRA2015-63708-R), and Colciencias for PhD scholarship granted to MsC. David Camilo Corrales.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Telematics Engineering GroupUniversity of CaucaPopayánColombia
  2. 2.Computer Science DepartmentCarlos III University of MadridLeganesSpain

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