Pavicic, Mirko and Overmyer, Kirk and Rehman, Attiq ur and Jones, Piet and Jacobson, Daniel and Himanen, Kristiina (2021) Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves. Plants, 10 (1). p. 158. ISSN 2223-7747
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Abstract
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.
Item Type: | Article |
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Uncontrolled Keywords: | Arabidopsis; high-throughput; plant phenotyping; imaging sensors; Botrytis; disease symptom; chlorophyll fluorescence |
Subjects: | STM Repository > Agricultural and Food Science |
Depositing User: | Managing Editor |
Date Deposited: | 02 Oct 2024 08:07 |
Last Modified: | 02 Oct 2024 08:07 |
URI: | http://classical.goforpromo.com/id/eprint/969 |