ArticleApplications in plant sciences
An image-based technique for automated root disease severity assessment using PlantCV.
Article in Applications in plant sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The early dodder gets the host: decoding the coiling patterns of Cuscuta campestris with automated image processing.Plant cell reports · 2024Article
- Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies.Frontiers in plant science · 2024Article
- Multiomics Research: Principles and Challenges in Integrated Analysis.Biodesign research · 2024Review
- The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review.Plant phenomics (Washington, D.C.) · 2024Review
- RGB image-based method for phenotyping rust disease progress in pea leaves using R.Plant methods · 2023Article
- An image-based technique for automated root disease severity assessment using PlantCV.Applications in plant sciencesArticle
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Authors and funding
4 authors.
Funding
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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 ( 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.