Affiliation:
1. Department of Plant Biology Michigan State University East Lansing Michigan 48824 USA
2. Plant Resilience Institute Michigan State University East Lansing Michigan 48824 USA
3. Department of Horticulture Michigan State University East Lansing Michigan 48824 USA
4. Department of Crop and Soil Sciences University of Georgia Athens Georgia 30602 USA
Abstract
AbstractPremisePlant disease severity assessments are used to quantify plant–pathogen interactions and identify disease‐resistant lines. One common method for disease assessment involves scoring tissue manually using a semi‐quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis.MethodsUsing PlantCV, we developed a Python‐based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot.ResultsQuantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (R2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output.DiscussionOverall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.
Subject
Plant Science,Ecology, Evolution, Behavior and Systematics
Cited by
2 articles.
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