Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance

Authors:
T. Rumpf;A. -K. Mahlein;U. Steiner;E. -C. Oerke;H. -W. Dehne;L. Plümer
Affiliations:
Institute of Geodesy and Geoinformation, Department of Geoinformation, University of Bonn, Meckenheimer Allee 172, D-53115 Bonn, Germany;Institute of Crop Science and Resource Conversation (INRES - Phytomedicine), University of Bonn, Nussallee 9, D-53115 Bonn, Germany;Institute of Crop Science and Resource Conversation (INRES - Phytomedicine), University of Bonn, Nussallee 9, D-53115 Bonn, Germany;Institute of Crop Science and Resource Conversation (INRES - Phytomedicine), University of Bonn, Nussallee 9, D-53115 Bonn, Germany;Institute of Crop Science and Resource Conversation (INRES - Phytomedicine), University of Bonn, Nussallee 9, D-53115 Bonn, Germany;Institute of Geodesy and Geoinformation, Department of Geoinformation, University of Bonn, Meckenheimer Allee 172, D-53115 Bonn, Germany
Venue:
Computers and Electronics in Agriculture
Year:
2010

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Abstract

Automatic methods for an early detection of plant diseases are vital for precision crop protection. The main contribution of this paper is a procedure for the early detection and differentiation of sugar beet diseases based on Support Vector Machines and spectral vegetation indices. The aim was (I) to discriminate diseased from non-diseased sugar beet leaves, (II) to differentiate between the diseases Cercospora leaf spot, leaf rust and powdery mildew, and (III) to identify diseases even before specific symptoms became visible. Hyperspectral data were recorded from healthy leaves and leaves inoculated with the pathogens Cercospora beticola, Uromyces betae or Erysiphe betae causing Cercospora leaf spot, sugar beet rust and powdery mildew, respectively for a period of 21 days after inoculation. Nine spectral vegetation indices, related to physiological parameters were used as features for an automatic classification. Early differentiation between healthy and inoculated plants as well as among specific diseases can be achieved by a Support Vector Machine with a radial basis function as kernel. The discrimination between healthy sugar beet leaves and diseased leaves resulted in classification accuracies up to 97%. The multiple classification between healthy leaves and leaves with symptoms of the three diseases still achieved an accuracy higher than 86%. Furthermore the potential of presymptomatic detection of the plant diseases was demonstrated. Depending on the type and stage of disease the classification accuracy was between 65% and 90%.