Thermal and Chlorophyll-Fluorescence Imaging Distinguish Plant-Pathogen Interactions at an Early Stage
Laury Chaerle, Dik Hagenbeek, Erik De Bruyne, Roland Valcke, Dominique Van Der Straeten
- Year
- 2004
- Citations
- 259
- Access
- Open access
Abstract
Different biotic stresses yield specific symptoms, owing to their distinct influence on a plant’s physiological status. To monitor early changes in a plant’s physiological status upon pathogen attack, chlorophyll fluorescence imaging (Chl-FI) and thermography, which respectively visualize photosynthetic efficiency and transpiration, were carried out in parallel for two fundamentally different plant–pathogen interactions. These non-destructive imaging techniques were able to visualize infections at an early stage, before damage appeared. Under growth-room conditions, a robotized set-up captured time series of visual, thermal and chlorophyll fluorescence images from infected regions on attached leaves. As a first symptom of the plant–virus interaction between resistant tobacco and tobacco mosaic virus (TMV), thermal imaging detected a local rise in temperature while Chl-FI monitored a co-localized increase in fluorescence intensity. Chl-FI also revealed pre-symptomatic high-intensity spots for the plant–fungus system sugar beet–Cercospora beticola. Concomitantly, spots of lower temperature were monitored with thermography, in marked contrast with our observations on TMV-infection in tobacco. Knowledge of disease signatures for different plant–pathogen interactions could allow early identification of emerging biotic stresses in crops, facilitating the containment of disease outbreaks. Presymptomatic monitoring clearly opens perspectives for quantitative screening for disease resistance, either on excised leaf pieces or attached leaves.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992