Regression Models for Spatial Data: An Example from Precision Agriculture

  • Georg Ruß
  • Rudolf Kruse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171)


The term 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 data-driven approach to agriculture poses a number of data mining problems. One of those is also an obviously important task in agriculture: yield prediction. Given a precise, geographically annotated data set for a certain field, can a season’s yield be predicted?

Numerous approaches have been proposed to solving this problem. In the past, classical regression models for non-spatial data have been used, like regression trees, neural networks and support vector machines. However, in a cross-validation learning approach, issues with the assumption of statistical independence of the data records appear. Therefore, the geographical location of data records should clearly be considered while employing a regression model. This paper gives a short overview about the available data, points out the issues with the classical learning approaches and presents a novel spatial cross-validation technique to overcome the problems and solve the aforementioned yield prediction task.


Precision Agriculture Data Mining Regression Modeling 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Georg Ruß
    • 1
  • Rudolf Kruse
    • 1
  1. 1.Otto-von-Guericke-Universität Magdeburg 

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