Phenotypic scoring of canola blackleg severity using machine learning image analysis

Abstract Canola blackleg is a fungal disease that causes significant yield loss and plant death of infected canola ( Brassica napus L., Brassica rapa L ., Brassica juncea L .) fields worldwide. One of the most effective methods for controlling blackleg is through the cultivation of resistant varieties. Consequently, quantitative scoring of blackleg disease severity of infected plants is a key metric for identifying and selecting resistant varieties. Traditionally, blackleg severity is scored by expert raters who evaluate disease in stem cross sections using established rating scales and reference images; however, human raters are expensive and inconsistent in their scoring. Here, we introduce a machine learning algorithm based on deep learning models that can score blackleg severity from cross‐section images of infected plants. We find that expert ratings are inconsistent across raters and across years for the same rater, creating substantial noise in resistance ratings. Meanwhile, our trained machine learning model performs more consistently than the median rater while maintaining a similar heritability as expert raters for the blackleg resistance trait. This model can be used to standardize blackleg resistance scoring across locations and years to improve canola breeding outcomes across affected regions.