Weakly supervised segmentation of leaf symptoms in field conditions
Résumé
Background
Crop diseases can cause significant yield losses. Deep learning models for
computer vision offers powerful tools to enhance human observation of
plant disease symptoms, for instance by using segmentation models to
mark out foliar symptoms. However, the most common and effective
architectures rely on a fully supervised learning that requires numerous,
costly and often unavailable, pixel-level annotated images.To overcome
this, we focus on weakly supervised segmentation [1]. The principle is to
generate segmentation masks from less informative annotations, such as
image-level labels, in order to train segmentation models with reduced
annotation effort.
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