4.5 Article

A Hyperspectral Library of Foliar Diseases of Wheat

Journal

PHYTOPATHOLOGY
Volume 111, Issue 9, Pages 1583-1593

Publisher

AMER PHYTOPATHOLOGICAL SOC
DOI: 10.1094/PHYTO-09-19-0335-R

Keywords

brown rust; data science; fungal pathogens; hyperspectral disease detection; machine learning; pathogen detection; powdery mildew; Septoria tritici blotch; spectral disease development; tan spot; techniques; wheat; yellow rust

Categories

Funding

  1. BASF Digital Farming
  2. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2070390732324]

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This study established a hyperspectral library of wheat foliar diseases, detected important turning points using spectral changes, and achieved high accuracy in disease detection and identification through machine learning methods.
This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (<= 99%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.

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