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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152. ACM Press, New York (1992)
Breiman, L.: Bagging predictors. Technical report, Department of Statistics, Univ. of California, Berkeley (1994)
Breiman, L.: Random forests. Machine Learning, 45(1):5–32 (2001)
Brenning, A.: Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Science 5(6), 853–862 (2005)
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)
Brenning, A., Lausen, B.: Estimating error rates in the classification of paired organs. Statistics in Medicine 27(22), 4515–4531 (2008)
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)
Bühlmann, P.: Bootstraps for time series. Statistical Science 17, 52–72 (2002)
Cressie, N.A.C.: Statistics for Spatial Data. Wiley, New York (1993)
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)
Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1, 131–156 (1997)
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)
Knudby, A., Brenning, A., LeDrew, E.: New approaches to modelling fish-habitat relationships. Ecological Modelling 221, 503–511 (2010)
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)
Leathwick, J.R., Elith, J., Francis, M.P., Hastie, T., Taylor, P.: Variation in demersal fish species richness in the oceans surrounding new zealand: an analysis using boosted regression trees. Marine Ecology Progress 321, 267–281 (2006)
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)
Pozdnoukhov, A., Foresti, L., Kanevski, M.: Data-driven topo-climatic mapping with machine learning methods. Natural Hazards 50(3), 497–518 (2009)
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2009), ISBN 3-900051-07-0
Ruß, G.: Data mining of agricultural yield data: A comparison of regression models. In: Perner, P. (ed.) Advances in Data Mining. Applications and Theoretical Aspects. LNCS, vol. 5633, pp. 24–37. Springer, Heidelberg (2009)
Ruß, G., Brenning, A.: Data mining in precision agriculture: Management of spatial information. In: Proceedings of IPMU 2010. Springer, Heidelberg (submitted for review 2010)
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)
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)
Ruß, G., Kruse, R., Wagner, P., Schneider, M.: Data mining with neural networks for wheat yield prediction. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol. 5077, pp. 47–56. Springer, Heidelberg (2008)
Stein, M.L.: Interpolation of Spatial Data: Some Theory for Kriging, June 1999. Springer Series in Statistics. Springer, Heidelberg (1999)
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinformatics 9(1), 307 (2008)
Strobl, C., Boulesteix, A.-L., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics 8(1), 25 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruß, G., Brenning, A. (2010). Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds) Advances in Intelligent Data Analysis IX. IDA 2010. Lecture Notes in Computer Science, vol 6065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13062-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-13062-5_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13061-8
Online ISBN: 978-3-642-13062-5
eBook Packages: Computer ScienceComputer Science (R0)