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Operational Research

, Volume 9, Issue 2, pp 121–140 | Cite as

A survey of data mining techniques applied to agriculture

  • A. Mucherino
  • Petraq Papajorgji
  • P. M. Pardalos
Review

Abstract

In this survey we present some of the most used data mining techniques in the field of agriculture. Some of these techniques, such as the k-means, the k nearest neighbor, artificial neural networks and support vector machines, are discussed and an application in agriculture for each of these techniques is presented. Data mining in agriculture is a relatively novel research field. It is our opinion that efficient techniques can be developed and tailored for solving complex agricultural problems using data mining. At the end of this survey we provide recommendations for future research directions in agriculture-related fields.

Keywords

Data mining Optimization k nearest neighbor k-means Support vector machines Artificial neural networks Agriculture 

Notes

Acknowledgment

This research has been partially supported by NSF grants.

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • A. Mucherino
    • 1
  • Petraq Papajorgji
    • 2
  • P. M. Pardalos
    • 2
  1. 1.LIXÉcole PolytechniquePalaiseauFrance
  2. 2.Center for Applied OptimizationUniversity of FloridaGainesvilleUSA

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