Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras

Titouan Lorieul (1, 2) , Katelin D. Pearson (3) , Elizabeth R. Ellwood (4) , Hervé Goëau (5) , Jean‐francois Molino (5) , Patrick W. Sweeney (6) , Jennifer M. Yost (7) , Joel Sachs (8) , Gil Nelson (3) , Pamela S. Soltis (9) , Pierre Bonnet (5) , Alexis Joly (1) , Erick Mata-Montero (10)

1 ZENITH - Scientific Data Management
2 UM - Université de Montpellier
3 FSU - Florida State University [Tallahassee]
4 Natural History Museum of Los Angeles County
5 UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations
6 Peabody Museum of Natural History
7 CAL POLY - California Polytechnic State University [San Luis Obispo]
8 AAFC - Agriculture and Agri-Food
9 Florida Museum of Natural History [Gainesville]
10 TEC - Costa Rica Institute of Technology [Cartago]

Mots clés: Herbarium data; Deep learning; Convolutional neural network; Natural history collections; Phenological stage annotation; Visual data classification

Domaines: Systématique, phylogénie et taxonomie; Ecosystèmes; Botanique; Biodiversité et Ecologie

Titouan Lorieul, Katelin D. Pearson, Elizabeth R. Ellwood, Hervé Goëau, Jean‐francois Molino, et al.. Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences, 2019, 7 (3), pp.e01233. ⟨10.1002/aps3.1233⟩. ⟨hal-02137748⟩