%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture non répertoriées par l'AERES
%A Sklab, Youcef
%A Ariaout Sklab, Hanane
%A Boujydah, Y.
%A Qacami, Y.
%A Prifti, Edi
%A Zucker, Jean-Daniel
%A Vignes Lebbe, R.
%A Chenin, Eric
%T Towards a deep learning-powered herbarium image analysis platform
%D 2024
%L fdi:010095714
%G ENG
%J Biodiversity Information Science and Standards
%@ 2535-0897
%P e135629 [4 ]
%R 10.3897/biss.8.135629
%U https://www.documentation.ird.fr/hor/fdi:010095714
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-12/010095714.pdf
%V 8
%W Horizon (IRD)
%X Global digitization efforts have archived millions of specimen scans worldwide in herbarium collections, which are essential for studying plant evolution and biodiversity.



ReColNat hosts, at present, over 10 million images. However, analyzing these datasets poses crucial challenges for botanical research. The application of deep learning in



biodiversity analyses, particularly in analyzing herbarium scans, has shown promising results across numerous tasks (Ariouat et al. 2023, Ariouat et al. 2024, Groom et al. 2023,



Sahraoui et al. 2023).
%$ 122INTAR ; 076BOTA