Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research

Katelin Pearson 1 Gil Nelson 1 Myla Aronson 2 Pierre Bonnet 3, 4 Laura Brenskelle 5 Charles Davis 6 Ellen Denny 7 Elizabeth Ellwood 8 Hervé Goëau 3 J Mason Heberling 9 Alexis Joly 10 Titouan Lorieul 10 Susan Mazer 11 Emily Meineke 12 Brian Stucky 13 Patrick Sweeney 14 Alexander White 15 Pamela Soltis 13
1 FSU - Florida State University [Tallahassee]
2 WINLAB Rutgers University
3 UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations
4 Cirad-BIOS - Département Systèmes Biologiques
5 University of Florida [Gainesville]
6 Harvard University [Cambridge]
7 University of Arizona
8 Natural History Museum of Los Angeles County
9 Carnegie Museum of Natural History [Pittsburgh]
10 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
11 UCSB - University of California [Santa Barbara]
12 UC Davis - University of California [Davis]
13 Florida Museum of Natural History
14 Peabody Museum of Natural History
15 Smithsonian Institution
Abstract : Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.
Keywords : Deep learning Climate change Phenology Machine learning Biodiversity
Type de document :
Article dans une revue
Liste complète des métadonnées

Littérature citée [51 références]  Voir  Masquer  Télécharger
https://hal.umontpellier.fr/hal-02573627
Contributeur : Yannick Brohard <>
Soumis le : vendredi 15 mai 2020 - 09:48:07
Dernière modification le : jeudi 10 décembre 2020 - 08:52:10

Fichier

Vignette du document
biaa044.pdf
Fichiers produits par l'(les) auteur(s)

Licence

Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Collections

B3ESTE | MIPS | LIRMM | ZENITH | INRAE | AMAP | INRIA | CNRS | CIRAD | UNIV-MONTPELLIER | IRD

Citation

Katelin Pearson, Gil Nelson, Myla Aronson, Pierre Bonnet, Laura Brenskelle, et al.. Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research. Bioscience, Oxford University Press (OUP), 2020, 70 (7), pp.610-620. ⟨10.1093/biosci/biaa044⟩. ⟨hal-02573627⟩

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

167

Téléchargements de fichiers

156