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@INPROCEEDINGS{russ2009icdm,
     author = {Ru{\ss}, Georg},
     editor = {Perner, Petra},
   keywords = {Data Mining, Modeling, Precision Agriculture, Regression},
      month = jul,
      title = {Data Mining of Agricultural Yield Data: A Comparison of Regression Models},
  booktitle = {Advances in Data Mining -- Applications and Theoretical Aspects},
     series = {LNAI},
     volume = {5633},
       year = {2009},
      pages = {24--37},
  publisher = {Springer},
   location = {Berlin, Heidelberg},
       issn = {0302-0743},
       isbn = {978-3-642-03066-6},
        url = {http://www.springerlink.com/content/3x41838425115j72/},
        doi = {10.1007/978-3-642-03067-3_3},
   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.}
}