Fusarium root rot (Fusarium cuneirostrum) and Fusarium wilt (F. oxysporum) are major constraints on common bean (Phaseolus vulgaris L.) production worldwide as limited genetic resistance is available in current cultivars. Wild accessions have greater genetic diversity than cultivated germplasm and could provide novel sources of resistance for these diseases. We screened 248 wild bean accessions in a greenhouse setting for responses to both Fusarium root rot and Fusarium wilt and recorded disease severity; resistance was observed in 21 and 16 accessions, respectively. PI 417775 was the most resistant accession to Fusarium root rot, exhibiting very few symptoms whereas accessions PI661845 and PI535441 were the most resistant to Fusarium wilt. Only accessions MX-GT-1 and PI535445 were resistant to both Fusarium root rot and Fusarium wilt.

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.