VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Accéder directement au contenu
Article Dans Une Revue (Data Paper) Scientific Data Année : 2023

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation

Gaetan Daubige
  • Fonction : Auteur
Lucas Bernigaud Samatan
  • Fonction : Auteur
Chrisbin James
Fernando Camacho
  • Fonction : Auteur
Wei Guo
Scott Chapman

Résumé

Applying deep learning to images of cropping systems provides new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification, of RGB images acquired at the ground level, into vegetation and background is a critical step in the estimation of several canopy traits. Current state of the art methodologies based on convolutional neural networks (CNNs) are trained on datasets acquired under controlled or indoor environments. These models are unable to generalize to real-world images and hence need to be fine-tuned using new labelled datasets. This motivated the creation of the VegAnn - Veg etation Ann otation - dataset, a collection of 3775 multi-crop RGB images acquired for different phenological stages using different systems and platforms in diverse illumination conditions. We anticipate that VegAnn will help improving segmentation algorithm performances, facilitate benchmarking and promote large-scale crop vegetation segmentation research.
Fichier principal
Vignette du fichier
2023_Madec_Scientificdata.pdf (2.89 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Licence : CC BY - Paternité

Dates et versions

hal-04102678 , version 1 (16-08-2023)

Licence

Paternité

Identifiants

Citer

Simon Madec, Kamran Irfan, Kaaviya Velumani, Frederic Baret, Etienne David, et al.. VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation. Scientific Data , 2023, 10 (1), pp.1-12. ⟨10.1038/s41597-023-02098-y⟩. ⟨hal-04102678⟩
64 Consultations
4 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More