Towards a deep learning-powered herbarium image analysis platform /Sklab, Youcef /Ariaout Sklab, Hanane Boujydah, Y. Qacami, Y. /Prifti, Edi /Zucker, Jean-Daniel Vignes Lebbe, R. /Chenin, Eric 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). 2024 text https://www.documentation.ird.fr/hor/fdi:010095714 fdi:010095714 Sklab Youcef, Ariaout Sklab Hanane, Boujydah Y., Qacami Y., Prifti Edi, Zucker Jean-Daniel, Vignes Lebbe R., Chenin Eric. Towards a deep learning-powered herbarium image analysis platform. 2024, 8, e135629 [4 ] EN