Résumé
Background
Crop diseases can cause significant yield losses. Deep learning models forcomputer vision offers powerful tools to enhance human observation ofplant disease symptoms, for instance by using segmentation models tomark out foliar symptoms. However, the most common and effectivearchitectures rely on a fully supervised learning that requires numerous,costly and often unavailable, pixel-level annotated images.To overcomethis, we focus on weakly supervised segmentation [1]. The principle is togenerate segmentation masks from less informative annotations, such asimage-level labels, in order to train segmentation models with reducedannotation effort.