Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture
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.
KeywordsPrecision Agriculture Spatial Data Mining Regression Spatial Cross-Validation Variable Importance
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- 2.Breiman, L.: Bagging predictors. Technical report, Department of Statistics, Univ. of California, Berkeley (1994)Google Scholar
- 3.Breiman, L.: Random forests. Machine Learning, 45(1):5–32 (2001)Google Scholar
- 5.Brenning, A., Itzerott, S.: Comparing classifiers for crop identification based on multitemporal landsat tm/etm data. In: Proceedings of the 2nd workshop of the EARSeL Special Interest Group Remote Sensing of Land Use and Land Cover, September 2006, pp. 64–71 (2006)Google Scholar
- 7.Brenning, A., Piotraschke, H., Leithold, P.: Geostatistical analysis of on-farm trials in precision agriculture. In: Ortiz, J.M., Emery, X. (eds.) GEOSTATS 2008, Proceedings of the Eighth International Geostatistics Congress, December 12, vol. 2, pp. 1131–1136 (2008)Google Scholar
- 9.Cressie, N.A.C.: Statistics for Spatial Data. Wiley, New York (1993)Google Scholar
- 10.Crone, S.F., Lessmann, S., Pietsch, S.: Forecasting with computational intelligence - an evaluation of support vector regression and artificial neural networks for time series prediction. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 3159–3166 (2006)Google Scholar
- 12.Huang, C., Yang, L., Wylie, B., Homer, C.: A strategy for estimating tree canopy density using landsat 7 etm+ and high resolution images over large areas. In: Proceedings of the Third International Conference on Geospatial Information in Agriculture and Forestry (2001)Google Scholar
- 14.Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall symposium on relevance, pp. 140–144. AAAI Press, Menlo Park (1994)Google Scholar
- 16.Lobell, D.B., Ortiz-Monasterio, J.I., Asner, G.P., Naylor, R.L., Falcon, W.P.: Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape. Agronomy Journal 97, 241–249 (2005)Google Scholar
- 17.Pozdnoukhov, A., Foresti, L., Kanevski, M.: Data-driven topo-climatic mapping with machine learning methods. Natural Hazards 50(3), 497–518 (2009)Google Scholar
- 18.R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2009), ISBN 3-900051-07-0Google Scholar
- 20.Ruß, G., Brenning, A.: Data mining in precision agriculture: Management of spatial information. In: Proceedings of IPMU 2010. Springer, Heidelberg (submitted for review 2010)Google Scholar
- 21.Ruß, G., Kruse, R., Schneider, M., Wagner, P.: Estimation of neural network parameters for wheat yield prediction. In: Bramer, M. (ed.) Proceedings of AI in Theory and Practice II, IFIP 2008, July 2008, vol. 276, pp. 109–118. Springer, Heidelberg (2008)Google Scholar
- 22.Ruß, G., Kruse, R., Schneider, M., Wagner, P.: Optimizing wheat yield prediction using different topologies of neural networks. In: Verdegay, J., Ojeda-Aciego, M., Magdalena, L. (eds.) Proceedings of IPMU 2008, June 2008, pp. 576–582. University of Málaga (2008)Google Scholar