Poster De Conférence Année : 2025

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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Dates et versions

hal-05478330 , version 1 (26-01-2026)

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Identifiants

  • HAL Id : hal-05478330 , version 1

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Romane Dubois, Lydia Bousset, Melen Leclerc, Nicolas Parisey, Alexis Joly. Weakly supervised segmentation of leaf symptoms in field conditions. Workshop Franco-Britannique organisé par le réseau « Modélisation et statistique pour la santé des animaux et des plantes », Oct 2025, Paris, France. ⟨hal-05478330⟩
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