Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture
| Type of publication: | Inproceedings |
| Citation: | russ2010ida |
| Booktitle: | Proceedings of IDA2010 |
| Series: | LNCS |
| Volume: | 6065 |
| Year: | 2010 |
| Pages: | 184--195 |
| Publisher: | Springer |
| Address: | Heidelberg |
| ISBN: | 978-3-642-13061-8 |
| URL: | http://www.springerlink.com/co... |
| DOI: | 10.1007/978-3-642-13062-5_18 |
| Abstract: | 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. |
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| Added by: | [GR] |
| Total mark: | 0 |
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