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.
Keywords Precision Agriculture - Spatial Data Mining - Regression - Spatial Cross-Validation - Variable Importance