The potential of automatic methods of classification to identify leaf diseases from multispectral images |
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Authors: | Sabine D Bauer Filip Kor? Wolfgang Förstner |
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Institution: | (1) Department of Photogrammetry, Institute of Geodesy and Geoinformation, University of Bonn, Nussallee 15, 53115 Bonn, Germany |
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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. |
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