Logan D. Pierz, Dilyn R. Heslinga, C. Robin Buell, Miranda J. Haus
{"title":"An image-based technique for automated root disease severity assessment using PlantCV","authors":"Logan D. Pierz, Dilyn R. Heslinga, C. Robin Buell, Miranda J. Haus","doi":"10.1002/aps3.11507","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Premise</h3>\n \n <p>Plant 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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Using 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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (<i>R</i><sup>2</sup> = 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.</p>\n </section>\n \n <section>\n \n <h3> Discussion</h3>\n \n <p>Overall, 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.</p>\n </section>\n </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11507","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applications in Plant Sciences","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aps3.11507","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
引用次数: 2
Abstract
Premise
Plant 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.
Methods
Using 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.
Results
Quantitative 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.
Discussion
Overall, 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.
期刊介绍:
Applications in Plant Sciences (APPS) is a monthly, peer-reviewed, open access journal promoting the rapid dissemination of newly developed, innovative tools and protocols in all areas of the plant sciences, including genetics, structure, function, development, evolution, systematics, and ecology. Given the rapid progress today in technology and its application in the plant sciences, the goal of APPS is to foster communication within the plant science community to advance scientific research. APPS is a publication of the Botanical Society of America, originating in 2009 as the American Journal of Botany''s online-only section, AJB Primer Notes & Protocols in the Plant Sciences.
APPS publishes the following types of articles: (1) Protocol Notes describe new methods and technological advancements; (2) Genomic Resources Articles characterize the development and demonstrate the usefulness of newly developed genomic resources, including transcriptomes; (3) Software Notes detail new software applications; (4) Application Articles illustrate the application of a new protocol, method, or software application within the context of a larger study; (5) Review Articles evaluate available techniques, methods, or protocols; (6) Primer Notes report novel genetic markers with evidence of wide applicability.