An image-based technique for automated root disease severity assessment using PlantCV.

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 ( R 2 = 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.

Plant phenotyping relevance

PlantCVを用いて根の病害重症度と根面積を自動画像推定するRootDSパイプラインを開発し、手動評価との相関で検証しており、植物表現型取得法が研究の中心です。

Code and data availability

The authors publicly released the RootDS Python analysis code and a subset of the root images on GitHub; the full dataset is available only upon request.