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Industrial Conference on Data Mining

ICDM 2009: Advances in Data Mining. Applications and Theoretical Aspects pp 24–37Cite as

Data Mining of Agricultural Yield Data: A Comparison of Regression Models

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5633))

Abstract

Nowadays, precision agriculture refers to the application of state-of-the-art GPS technology in connection with small-scale, sensor-based treatment of the crop. This introduces large amounts of data which are collected and stored for later usage. Making appropriate use of these data often leads to considerable gains in efficiency and therefore economic advantages. However, the amount of data poses a data mining problem – which should be solved using data mining techniques. One of the tasks that remains to be solved is yield prediction based on available data. From a data mining perspective, this can be formulated and treated as a multi-dimensional regression task. This paper deals with appropriate regression techniques and evaluates four different techniques on selected agriculture data. A recommendation for a certain technique is provided.

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Authors and Affiliations

  1. Otto-von-Guericke-Universität Magdeburg, Germany

    Georg Ruß

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  1. Georg Ruß
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Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Körnerstr. 10, 04107, Leipzig, Deutschland

    Petra Perner

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Ruß, G. (2009). Data Mining of Agricultural Yield Data: A Comparison of Regression Models. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_3

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  • DOI: https://doi.org/10.1007/978-3-642-03067-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03066-6

  • Online ISBN: 978-3-642-03067-3

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