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dc.contributor.authorDavis, Charles C.en_US
dc.contributor.authorChamp, Julienen_US
dc.contributor.authorPark, Daniel S.en_US
dc.contributor.authorBreckheimer, Ianen_US
dc.contributor.authorLyra, Goia M.en_US
dc.contributor.authorXie, Junxien_US
dc.contributor.authorJoly, Alexisen_US
dc.contributor.authorTarapore, Dharmeshen_US
dc.contributor.authorEllison, Aaron M.en_US
dc.contributor.authorBonnet, Pierreen_US
dc.date.accessioned2020-09-18T15:40:56Z
dc.date.available2020-09-18T15:40:56Z
dc.identifier.citationCharles C Davis, Julien Champ, Daniel S Park, Ian Breckheimer, Goia M Lyra, Junxi Xie, Alexis Joly, Dharmesh Tarapore, Aaron M Ellison, Pierre Bonnet. "A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN." Frontiers in Plant Science, Volume 11, https://doi.org/10.3389/fpls.2020.01129
dc.identifier.issn1664-462X
dc.identifier.urihttps://hdl.handle.net/2144/41399
dc.description.abstractPhenology—the timing of life-history events—is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from >3,000 specimens of six common wildflower species of the eastern United States (Anemone canadensis L., A. hepatica L., A. quinquefolia L., Trillium erectum L., T. grandiflorum (Michx.) Salisb., and T. undulatum Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with >87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with ≥90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant responses to ongoing climatic change.en_US
dc.description.urihttps://www.frontiersin.org/articles/10.3389/fpls.2020.01129/full
dc.language.isoen_US
dc.publisherFrontiers Media SAen_US
dc.relation.ispartofFrontiers in Plant Science
dc.rightsCopyright © 2020 Davis, Champ, Park, Breckheimer, Lyra, Xie, Joly, Tarapore, Ellison and Bonnet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/.
dc.subjectPlant biologyen_US
dc.titleA new method for counting reproductive structures in digitized herbarium specimens using mask R-CNNen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.3389/fpls.2020.01129
pubs.elements-sourcecrossrefen_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, Administrationen_US
pubs.publication-statusPublished onlineen_US
dc.date.online2020-07-31
dc.description.oaversionPublished version
dc.identifier.mycv567722


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Copyright © 2020 Davis, Champ, Park, Breckheimer, Lyra, Xie, Joly, Tarapore, Ellison and Bonnet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as Copyright © 2020 Davis, Champ, Park, Breckheimer, Lyra, Xie, Joly, Tarapore, Ellison and Bonnet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.