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Plant disease detection by hyperspectral imaging: from the lab to the field

Published online by Cambridge University Press:  01 June 2017

A-K. Mahlein ,
M. T. Kuska ,
S. Thomas ,
D. Bohnenkamp ,
E. Alisaac ,
J. Behmann ,
M. Wahabzada  and
K. Kersting
A-K. Mahlein*
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
M. T. Kuska
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
S. Thomas
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
D. Bohnenkamp
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
E. Alisaac
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
J. Behmann
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
M. Wahabzada
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
K. Kersting
Affiliation:
Department of Informatics, TU Dortmund, Germany
*
†E-mail: amahlein@uni-bonn.de
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Abstract

The detection and identification of plant diseases is a fundamental task in sustainable crop production. An accurate estimate of disease incidence, disease severity and negative effects on yield quality and quantity is important for precision crop production, horticulture, plant breeding or fungicide screening as well as in basic and applied plant research. Particularly hyperspectral imaging of diseased plants offers insight into processes during pathogenesis. By hyperspectral imaging and subsequent data analysis routines, it was possible to realize an early detection, identification and quantification of different relevant plant diseases. Depending on the measuring scale, even subtle processes of defence and resistance mechanism of plants could be evaluated. Within this scope, recent results from studies in barley, wheat and sugar beet and their relevant foliar diseases will be presented.

Keywords

Plant diseasescrop protectionresistancehyperspectral imagingmachine learning
Type
Crop Protection
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 238 - 243
Copyright
© The Animal Consortium 2017 

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