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Agribusiness Intelligence: Grape Production Forecast Using Data Mining Techniques

  • Rosana Cavalcante de Oliveira
  • João Mendes-Moreira
  • Carlos Abreu Ferreira
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 747)

Abstract

The agribusiness volatility is related to the uncertainty of the environment, rising demand, falling prices and new technologies. However, generation of agriculture data has increased over past years and can be used for a growing number of applications of data mining techniques in agriculture. The multidisciplinary approach of integrating computer science with agriculture will support the necessary decisions to be taken in order to mitigate risks and maximize profits. The present study analyzes different methods of regression applied in the study case of grapes production forecast. The selected methods were multivariate linear regression, regression trees, lasso and random forest. Their performance were compared against the predictions obtained by the company through the mean squared error and the coefficient of variation. The four regression methods used obtained better predictive results than the method used by the company with statistical significance < 0.5%.

Keywords

Production forecast Data mining Agribusiness Grapes 

Notes

Acknowledgements

Rosana Cavalcante acknowledges the grant from project Smartfarming (POCI-01-0247-FEDER-018029), co-financed by the ERDF through COMPETE 2020 under P2020 Partnership Agreement and by the Portuguese Agency ANI. This work is also funded by the ERDF through COMPETE 2020 within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rosana Cavalcante de Oliveira
    • 1
  • João Mendes-Moreira
    • 1
  • Carlos Abreu Ferreira
    • 1
  1. 1.LIAAD-INESC TECCampus da FEUPPortoPortugal

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