Submitted to: Plant Pathology Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/11/2024
Publication Date: 11/4/2024
Citation: Todd, O.E., Hanson, L.E., Dorn, K.M. 2024. A standard area diagram for Fusarium yellows rating in sugar beet (Beta vulgaris L.). Plant Pathology Journal . 74(2):422-430. https://doi.org/10.1111/ppa.14028.
DOI: https://doi.org/10.1111/ppa.14028

Interpretive Summary: Sugar beet crops are highly susceptible to many diseases, specifically a fungus that causes Fusarium yellows disease. Fusarium yellows can reduce yield and extractable sugar, which sugar beet is grown for, primarily in the United States and Europe. When researchers are judging beet lines for resistance, an accurate and precise rating method is required. As part of creating reliable rating methods that can be repeated across the country and the world, USDA-ARS researchers have created a standard area diagram to help field workers accurately and reliably choose sugar beet plants in the field that are resistant to the Fusarium yellows disease using photographs with a rating scale. We recommend that sugar beet researchers and field workers use the version 2 standard area diagram in the field, and also report that there is potential to eliminate strenuous hand rating with imaging technology.

Technical Abstract: Members of the Fusarium oxysporum species complex are pathogens of sugar beet (Beta vulgaris L.) that causes Fusarium yellows. Fusarium yellows can reduce plant stand, yield, and extractable sugar. Accurate and precise rating methods are required for breeding programs selecting resistant germplasm and for disease assessment in fields. As part of developing rating methods, two standard area diagram versions using a highly virulent member of the Fusarium oxysporum species complex, Fusarium oxysporum strain F19 were created and tested on up to 18 inexperienced raters. Version 1 raters evaluated 75 images of Fusarium yellows symptoms in sugar beet with 9 category scales and a written description of the symptoms. Version 2 improved upon version 1 for ease of use, and in increasing category availability. There were no statistical differences in Lin’s Concordance Correlation Coefficient (LCCC) values to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, 'c = 0.95 and 0.96 for version 1 and 2 respectively). In addition, five naïve Bayesian machine learning models which used pixel classification as a means to determine disease score were tested for congruency to the human estimates in version 2. Root mean square error was lowest compared to the “true” values for the unweighted model and a model where the necrotic tissue was given a 2x weight (12.4 and 12.6, respectively).