Advertisement

Regression Models for Spatial Data: An Example from Precision Agriculture

  • Georg Ruß
  • Rudolf Kruse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171)

Abstract

The term precision agriculture refers to the application of state-of-the-art GPS technology in connection with small-scale, sensor-based treatment of the crop. This data-driven approach to agriculture poses a number of data mining problems. One of those is also an obviously important task in agriculture: yield prediction. Given a precise, geographically annotated data set for a certain field, can a season’s yield be predicted?

Numerous approaches have been proposed to solving this problem. In the past, classical regression models for non-spatial data have been used, like regression trees, neural networks and support vector machines. However, in a cross-validation learning approach, issues with the assumption of statistical independence of the data records appear. Therefore, the geographical location of data records should clearly be considered while employing a regression model. This paper gives a short overview about the available data, points out the issues with the classical learning approaches and presents a novel spatial cross-validation technique to overcome the problems and solve the aforementioned yield prediction task.

Keywords

Precision Agriculture Data Mining Regression Modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anselin, L.: Spatial Econometrics, pp. 310–330. Basil Blackwell, Oxford (2001)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Breiman, L.: Bagging predictors. Technical report, Department of Statistics, Univ. of California, Berkeley (1994)Google Scholar
  4. 4.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Corwin, D.L., Lesch, S.M.: Application of soil electrical conductivity to precision agriculture: Theory, principles, and guidelines. Agron J. 95(3), 455–471 (2003)Google Scholar
  6. 6.
    Cressie, N.A.C.: Statistics for Spatial Data. Wiley, New York (1993)Google Scholar
  7. 7.
    Griffith, D.A.: Spatial Autocorrelation and Spatial Filtering. In: Advances in Spatial Science, Springer, New York (2003)Google Scholar
  8. 8.
    Gunn, S.R.: Support vector machines for classification and regression. Technical Report, School of Electronics and Computer Science, University of Southampton, Southampton, U.K (1998)Google Scholar
  9. 9.
    Liu, J., Miller, J.R., Haboudane, D., Pattey, E.: Exploring the relationship between red edge parameters and crop variables for precision agriculture. In: 2004 IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. 1276–1279 (2004)Google Scholar
  10. 10.
    Goodchild, M., Anselin, L., Appelbaum, R., Harthorn, B.: Toward spatially integrated social science. International Regional Science Review 23, 139–159 (2000)Google Scholar
  11. 11.
    Meier, U.: Entwicklungsstadien mono- und dikotyler Pflanzen. Biologische Bundesanstalt für Land- und Forstwirtschaft, Braunschweig, Germany (2001)Google Scholar
  12. 12.
    Mejía-Guevara, I., Kuri-Morales, Á.: Evolutionary feature and parameter selection in support vector regression. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 399–408. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Middleton, E.M., Campbell, P.K.E., Mcmurtrey, J.E., Corp, L.A., Butcher, L.M., Chappelle, E.W.: ”Red edge” optical properties of corn leaves from different nitrogen regimes. In: 2002 IEEE International Geoscience and Remote Sensing Symposium, vol. 4, pp. 2208–2210 (2002)Google Scholar
  14. 14.
    Moran, P.A.P.: Notes on continuous stochastic phenomena. Biometrika 37, 17–33 (1950)zbMATHMathSciNetGoogle Scholar
  15. 15.
    Neeteson, J.J.: Nitrogen Management for Intensively Grown Arable Crops and Field Vegetables, ch. 7, pp. 295–326. CRC Press, Haren (1995)Google Scholar
  16. 16.
    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
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    Ruß, G., Kruse, R., Schneider, M., Wagner, P.: Estimation of neural network parameters for wheat yield prediction. In: Bramer, M. (ed.) AI in Theory and Practice II, July 2008. Proceedings of IFIP-2008, vol. 276, pp. 109–118. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    Ruß, G., Kruse, R., Schneider, M., Wagner, P.: Visualization of agriculture data using self-organizing maps. In: Allen, T., Ellis, R., Petridis, M. (eds.) Applications and Innovations in Intelligent Systems, January 2009. Proceedings of AI-2008, vol. 16, pp. 47–60, BCS SGAI. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Technical report, Statistics and Computing (1998)Google Scholar
  23. 23.
    Stein, M.L.: Interpolation of Spatial Data: Some Theory for Kriging (Springer Series in Statistics). Springer, Heidelberg (June 1999)zbMATHGoogle Scholar
  24. 24.
    Weigert, G.: Data Mining und Wissensentdeckung im Precision Farming - Entwicklung von ökonomisch optimierten Entscheidungsregeln zur kleinräumigen Stickstoff-Ausbringung. PhD thesis, TU München (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Georg Ruß
    • 1
  • Rudolf Kruse
    • 1
  1. 1.Otto-von-Guericke-Universität Magdeburg 

Personalised recommendations