Investigation into the classification of diseases of sugar beet leaves using multispectral images

S.D. Bauer; F. Korč; W. Förstner

Investigation into the classification of diseases of sugar beet leaves using multispectral images

于Precision agriculture '09

著者：

S.D. Bauer

S.D. Bauer University of Bonn, Institute of Geodesy and Geoinformation, Department of Photogrammetty, Nussallee 15, D-53115 Bonn, Germany; sabine.bauer@uni-bonn

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F. Korč

F. Korč University of Bonn, Institute of Geodesy and Geoinformation, Department of Photogrammetty, Nussallee 15, D-53115 Bonn, Germany; sabine.bauer@uni-bonn

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, and

W. Förstner

W. Förstner University of Bonn, Institute of Geodesy and Geoinformation, Department of Photogrammetty, Nussallee 15, D-53115 Bonn, Germany; sabine.bauer@uni-bonn

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类型:: 章节

Page Range:: 261–270

DOI:: https://doi.org/10.1163/9789086866649_032

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Abstract

This paper reports on methods for the automatic detection and classification of leaf diseases based on high resolution multispectral images. Leaf diseases are economically important as they could cause a yield loss. Early and reliable detection of leaf diseases therefore is of utmost practical relevance - especially in the context of precision agriculture for localized treatment with fungicides. Our interest is the analysis of sugar beet due to their economical impact. Leaves of sugar beet may be infected by several diseases, such as rust (Uromyces betae), powdery mildew (Erysiphe betae) and other leaf spot diseases (Cercospora beticola and Ramularia beticola). In order to obtain best classification results we apply conditional random fields. In contrast to pixel based classifiers we are able to model the local context and contrary to object centred classifiers we simultaneously segment and classify the image. In a first investigation we analyse multispectral images of single leaves taken in a lab under well controlled illumination conditions. The photographed sugar beet leaves are healthy or either infected with the leaf spot pathogen Cercospora beticola or with the rust fungus Uromyces betae. We compare the classification methods pixelwise maximum posterior classification (MAP), objectwise MAP as soon as global MAP and global maximum posterior marginal classification using the spatial context within a conditional random field model.

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Precision agriculture '09

ISBN:: 9789086866649

出版社:: Wageningen Academic

印刷出版日期:: 01 Jan 2009

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Investigation into the classification of diseases of sugar beet leaves using multispectral images

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