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Precision Agriculture

, Volume 12, Issue 3, pp 361–377 | Cite as

The potential of automatic methods of classification to identify leaf diseases from multispectral images

  • Sabine D. Bauer
  • Filip Korč
  • Wolfgang Förstner
Article

Abstract

Three methods of automatic classification of leaf diseases are described based on high-resolution multispectral stereo images. Leaf diseases are economically important as they can cause a loss of yield. Early and reliable detection of leaf diseases has important practical relevance, especially in the context of precision agriculture for localized treatment with fungicides. We took stereo images of single sugar beet leaves with two cameras (RGB and multispectral) in a laboratory under well controlled illumination conditions. The leaves were either healthy or infected with the leaf spot pathogen Cercospora beticola or the rust fungus Uromyces betae. To fuse information from the two sensors, we generated 3-D models of the leaves. We discuss the potential of two pixelwise methods of classification: k-nearest neighbour and an adaptive Bayes classification with minimum risk assuming a Gaussian mixture model. The medians of pixelwise classification rates achieved in our experiments are 91% for Cercospora beticola and 86% for Uromyces betae. In addition, we investigated the potential of contextual classification with the so called conditional random field method, which seemed to eliminate the typical errors of pixelwise classification.

Keywords

Pattern recognition Gaussian mixture model (GMM) Conditional random field (CRF) k-nearest neighbour Sugar beet Sensor fusion 

Notes

Acknowledgments

This research is funded by the DFG Post Graduate Program 722 ‘Use of information technologies for precision crop protection’ and partly by EU-STREP 027113 eTRIMS ‘E-Training for Interpreting Images of Man-Made Scenes’. The authors are grateful to the Department of Phytomedicine at the University Bonn for their assistance.

References

  1. Bauer, S. D., Korč, F., & Förstner, W. (2009). Investigation into the classification of diseases of sugar beet leaves using multispectral images. In E. J. van Henten, D. Goense, & C. Lokhorst (Eds.), Precision agriculture ‘09 (pp. 229–238). Wageningen: Wageningen Academic Press.Google Scholar
  2. Bilmes, J. A. (1998). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and Hidden Markov models. Technical report. Berkeley: University of California at Berkeley, International Computer Science Institute, Department of Electrical Engineering and Computer Science.Google Scholar
  3. Chekuri, C., Khanna, S., Naor, J., & Zosin, L. (2001). Approximation algorithms for the metric labelling problem via a new linear programming formulation. In Proceedings of the twelfth annual ACM-SIAM symposium on discrete algorithms (pp. 109–118). Washington, DC: ACM/SIAM.Google Scholar
  4. Fukunaga, K. (1972). Introduction to statistical pattern recognition. New York: Academic Press.Google Scholar
  5. Huang, K.-Y. (2007). Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Computers and Electronics in Agriculture, 57, 3–11.CrossRefGoogle Scholar
  6. Korč, F., & Förstner, W. (2008). Approximate parameter learning in conditional random fields: An empirical investigation. In G. Rigoll (Ed.), Pattern recognition. LNCS 5096 (pp. 11–20). Berlin: Springer.Google Scholar
  7. Koster, A. M. C. A., van Hoesel, S. P. M., & Kolen, A. W. J. (1998). The partial constraint satisfaction problem: Facets and lifting theorems. Operations Research Letters, 23, 89–97.CrossRefGoogle Scholar
  8. Kumar, S., August, J., & Hebert, M. (2005). Exploiting inference for approximate parameter learning in discriminative fields: An empirical study. In A. Rangarajan, B. Vemuri, & A. L. Yuille (Eds.), Energy minimization methods in computer vision and pattern recognition. LNCS 3757 (pp. 153–168). Berlin: Springer.Google Scholar
  9. Kumar, S., & Hebert, M. (2006). Discriminative random fields. International Journal of Computer Vision, 68, 179–201.CrossRefGoogle Scholar
  10. Läbe, T., & Förstner, W. (2006). Automatic relative orientation of images. In L. Gründig & M. O. Altan (Eds.), Proceedings of the 5th Turkish-German joint geodetic days, Berlin.Google Scholar
  11. Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In C. E. Brodley & A. P. Danyluk (Eds.) Proceedings of the 18th international conference on machine learning (pp. 282–289). Massachusetts: Morgan Kaufmann.Google Scholar
  12. Lemaire, C. (2008). Aspects of the DSM production with high resolution images. In J. Chen, J. Jiang, & S. Nayak (Eds.) Proceedings of the XXIst ISPRS congress, Beijing, China.Google Scholar
  13. Mahlein, A.-K., Steiner, U., Dehne, H.-W., & Oerke, E.-C. (2010). Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture, 11, 413–431.CrossRefGoogle Scholar
  14. Pydipati, R., Burks, T. F., & Lee, W. S. (2006). Identification of citrus disease using color texture features and discriminant analysis. Computers and Electronics in Agriculture, 52, 49–59.CrossRefGoogle Scholar
  15. Sanyal, P., & Patel, S. C. (2008). Pattern recognition method to detect two diseases in rice plants. The Imaging Science Journal, 56, 319–325.CrossRefGoogle Scholar
  16. Schlesinger, M. I. (1976). Sintaksicheskiy analiz dvumernykh zritelnikh singnalov v usloviyakh pomekh (Syntactic analysis of two-dimensional visual signals in noisy conditions). Kibernetika, 4, 113–130.Google Scholar
  17. Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., et al. (2008). Comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1068–1080.PubMedCrossRefGoogle Scholar
  18. Wainwright, M. J., Jaakkola, T. S., & Willsky, A. S. (2005). MAP estimation via agreement on trees: Message-passing and linear programming. IEEE Transactions on Information Theory, 51, 3697–3717.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Sabine D. Bauer
    • 1
  • Filip Korč
    • 1
  • Wolfgang Förstner
    • 1
  1. 1.Department of PhotogrammetryInstitute of Geodesy and Geoinformation, University of BonnBonnGermany

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