Image-based methods to score fungal pathogen symptom progression and severity in excised Arabidopsis leaves

Mirko Pavicic, Kirk Overmyer, Attiq ur Rehman, Piet Jones, Daniel Jacobson, Kristiina Himanen

Research output: Contribution to journalArticlepeer-review


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 languageEnglish
Article number158
Issue number1
Number of pages14
Publication statusPublished - Jan 2021
MoE publication typeA1 Journal article-refereed

Fields of Science

  • Arabidopsis
  • Botrytis
  • chlorophyll fluorescence
  • disease symptom
  • high-throughput
  • imaging sensors
  • plant phenotyping
  • 11831 Plant biology

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