Data Mining in Precision Agriculture: Management of Spatial Information

  • Georg Ruß
  • Alexander Brenning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6178)

Abstract

Precision Agriculture is the application of state-of-the-art GPS technology in connection with site-specific, sensor-based treatment of the crop. It can also be described as a data-driven approach to agriculture, which is strongly connected with a number of data mining problems. One of those is also an inherently important task in agriculture: yield prediction. The question is: can a field’s yield be predicted in-season using available geo-coded data sets?

In the past, a number of approaches have been proposed towards this problem. Often, a broad variety of regression models for non-spatial data have been used, like regression trees, neural networks and support vector machines. But in a cross-validation learning approach, issues with the assumption of the data records’ statistical independence keep emerging. Hence, the geographical location of data records should clearly be considered while establishing a regression model and assessing its predictive performance. This paper gives a short overview of the available data, points out in detail the main issue with the classical learning approaches and presents a novel spatial cross-validation technique to overcome the problems with the classical approach towards the aforementioned yield prediction task.

Keywords

Precision Agriculture Spatial Data Mining Regression Spatial Cross-Validation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Georg Ruß
    • 1
  • Alexander Brenning
    • 2
  1. 1.Otto-von-Guericke-Universität MagdeburgGermany
  2. 2.University of WaterlooCanada

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