TY  - CONF
ID  - russ2009icdm
T1  - Data Mining of Agricultural Yield Data: A Comparison of Regression Models
A1  - Ruß, Georg
ED  - Perner, Petra
TI  - Advances in Data Mining -- Applications and Theoretical Aspects
T3  - LNAI
Y1  - 2009
VL  - 5633
SP  - 24
EP  - 37
PB  - Springer
CY  - Berlin, Heidelberg
SN  - 978-3-642-03066-6
SN  - 0302-0743
UR  - http://www.springerlink.com/content/3x41838425115j72/
M2  - doi: 10.1007/978-3-642-03067-3_3
KW  - Data Mining
KW  - Modeling
KW  - Precision Agriculture
KW  - Regression
N2  - 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.
ER  -