AbstractMembers of the Fusarium oxysporum species complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield and extractable sugar. Improving host plant resistance against Fusarium‐induced diseases, like Fusarium yellows, represents an important long‐term breeding target in sugar beet breeding programmes. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected with Fusarium oxysporum strain F19. Each version was tested using inexperienced raters. Version 1 and the improved version 2 SAD showed no statistical differences in Lin's concordance correlation coefficient (LCC) values, which was used to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, c = 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine‐learning models that used pixel classification to determine disease score were tested for congruency to human estimates in SAD version 2. Root mean square error was lowest compared to the ‘true’ values for the unweighted model and a model where necrotic tissue was given a 2× weight (12.4 and 12.6, respectively). The creation of this SAD enables breeding programmes to make consistent, accurate disease ratings regardless of personnel's previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows.