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Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective

  • Autor/in: Thomas, S., M.T. Kuska, D. Bohnenkamp, A. Brugger, E. Alisaac, M. Wahabzada, J. Behmann, A.-K. Mahlein
  • Jahr: 2018
  • Zeitschrift: Journal of Plant Disease and Protection 125
  • Seite/n: 5-20
  • ISBN: doi.org/10.1007/s41348-017-0124-6

Abstract

The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innova- tive and technology-based optical methods for plant dis- ease detection. In contrast to common visual rating and detection methods, optical sensors are able to measure pathogen-induced changes in the plant physiology non-in- vasively and objectively. Several studies showed that especially hyperspectral sensors are valuable tools for disease detection, identification and quantification on dif- ferent scales from the tissue to the canopy level. This review describes the basic principles of hyperspectral measurements and different types of available hyperspec- tral sensors. Possible applications of hyperspectral sensors on different scales for disease detection and plant protec- tion are discussed and evaluated. The advantages and dis- advantages on each particular scale, as well as the impact of external factors, such as: light, wind, viewing angle, for measurements in laboratories, greenhouses and fields, are critically assessed in order to support researchers and agriculture technicians. Additionally, a comprehensive lit- erature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non-invasive measurement systems. This highlights advantages of hyperspectral sensors when investigating plant–pathogen interactions through multiple examples. By some approaches, detection before visible symptoms appear is feasible. The potential of hyperspectral sensors as a tool for disease identification and quantifica- tion, based on disease characteristic changes in the plants spectral signature, is discussed as well. The review is concluded with an overview on different data analysis methods, which are required to extract key information from gathered hyperspectral datasets.
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