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Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Journal 13 May 2020 France, United States Publisher:Oxford University Press (OUP)Journal:BioScience, volume 70, pages 610-620 (issn: 0006-3568, eissn: 1525-3244, Copyright policy )
Authors: Pearson, Katelin; Nelson, Gil; Aronson, Myla; Bonnet, Pierre; Brenskelle, Laura; Davis, Charles; Denny, Ellen; +11 Authors

doi: 10.1093/biosci/biaa044

pmid: 32665738

pmc: PMC7340542

Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research

Abstract

AbstractMachine 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.

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Keywords

[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, herbier, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Biologist's Toolbox, [SDV.EE.ECO] Life Sciences [q-bio]/Ecology, environment/Ecosystems, Climate change, apprentissage machine, biodiversity, changement climatique, collecte de données, Ecology, U10 - Informatique, mathématiques et statistiques, http://aims.fao.org/aos/agrovoc/c_5957, http://aims.fao.org/aos/agrovoc/c_49834, Phylogenetics and taxonomy, http://aims.fao.org/aos/agrovoc/c_5959, Biodiversity, Biological Sciences, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, phénologie, climate change, machine learning, C30 - Documentation et information, Phenology, http://aims.fao.org/aos/agrovoc/c_2128, environment/Ecosystems, http://aims.fao.org/aos/agrovoc/c_10289, F40 - Écologie végétale, traitement des données, phenology, 333, [SDV.BV.BOT] Life Sciences [q-bio]/Vegetal Biology/Botanics, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Machine learning, [SDV.BID.SPT] Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, http://aims.fao.org/aos/agrovoc/c_1666, http://aims.fao.org/aos/agrovoc/c_3565, 580, collection botanique, stade de développement végétal, deep learning, Deep learning, traitement numérique d'image, http://aims.fao.org/aos/agrovoc/c_9000033, Climate Action, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, http://aims.fao.org/aos/agrovoc/c_5774, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Environmental Sciences

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 75
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
75
Top 1%
Top 10%
Top 1%
Green
hybrid
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