Towards a Deep Learning-Powered Herbarium Image Analysis Platform - Université des Antilles
Journal Articles Biodiversity Information Science and Standards Year : 2024

Towards a Deep Learning-Powered Herbarium Image Analysis Platform

Abstract

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
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hal-04686312 , version 1 (04-09-2024)

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Youcef Sklab, Hanane Ariouat, Youssef Boujydah, Yassine Qacami, Edi Prifti, et al.. Towards a Deep Learning-Powered Herbarium Image Analysis Platform. Biodiversity Information Science and Standards, 2024, 8, ⟨10.3897/biss.8.135629⟩. ⟨hal-04686312⟩
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