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A survey of data mining techniques applied to agriculture

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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.

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Acknowledgment

This research has been partially supported by NSF grants.

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Authors and Affiliations

  1. LIX, École Polytechnique, Palaiseau, France

    A. Mucherino

  2. Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Petraq Papajorgji & P. M. Pardalos

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  1. A. Mucherino
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Correspondence to Petraq Papajorgji.

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Mucherino, A., Papajorgji, P. & Pardalos, P.M. A survey of data mining techniques applied to agriculture. Oper Res Int J 9, 121–140 (2009). https://doi.org/10.1007/s12351-009-0054-6

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