TY  - CONF
ID  - russ2010ida
T1  - Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture
A1  - Ruß, Georg
A1  - Brenning, Alexander
ED  - R. Cohen, Paul
ED  - M. Adams, Niall
ED  - Berthold, Michael R.
TI  - Proceedings of IDA2010
T3  - LNCS
Y1  - 2010
VL  - 6065
SP  - 184
EP  - 195
PB  - Springer
AD  - Heidelberg
SN  - 978-3-642-13061-8
UR  - http://www.springerlink.com/content/p63pn0561u18r34w/
M2  - doi: 10.1007/978-3-642-13062-5_18
N2  - Precision Agriculture applies state-of-the-art GPS technology in connection with site-specific, sensor-based crop management. 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. Given a yield prediction model, which of the predictor variables are the important ones? 
In the past, a number of approaches have been proposed towards this problem. For yield prediction, a broad variety of regression models for non-spatial data can be adapted for spatial data using a novel spatial cross-validation technique. Since this procedure is at the core of variable importance assessment, it will be briefly introduced here. Given this spatial yield prediction model, a novel approach towards assessing a variable’s importance will be presented. It essentially consists of picking each of the predictor variables, one at a time, permutating its values in the test set and observing the deviation of the model’s RMSE. This article uses two real-world data sets from precision agriculture and evaluates the above procedure.
ER  -