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 confer-ring 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.
| Original language | English |
|---|---|
| Article number | 158 |
| Pages (from-to) | 1-14 |
| Number of pages | 14 |
| Journal | Plants |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2021 |
Funding
Acknowledgments: This project was supported by the Academy of Finland (Suomen Akatemia #283138, #256094, and #250972) and Becas Chile from the Chilean National Agency for Research and Development (ANID). This research was also supported by the Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program of the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science under award number DE-AC05-00OR22725. This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http:// energy.gov/downloads/doe-public-access-plan). Funding: This research was funded by the Academy of Finland (Suomen Akatemia) grant numbers #283138, #256094, and #250972.
Keywords
- Arabidopsis
- Botrytis
- Chlorophyll fluorescence
- Disease symptom
- High-throughput
- Imaging sensors
- Plant phenotyping