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


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.


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



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.


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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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