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A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN
- Source :
- Frontiers in Plant Science, Vol 11 (2020), Frontiers in Plant Science, Frontiers in Plant Science, 2020, 11, ⟨10.3389/fpls.2020.01129⟩, Frontiers in Plant Science, Frontiers, 2020, 11, ⟨10.3389/fpls.2020.01129⟩
- Publication Year :
- 2020
- Publisher :
- Frontiers Media S.A., 2020.
-
Abstract
- International audience; Phenology—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.
- Subjects :
- 0106 biological sciences
Collection botanique
Plant Science
[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy
01 natural sciences
digitized herbarium specimen
regional convolutional neural network
Original Research
Herbier
0303 health sciences
biology
Wildflower
Phenology
U10 - Informatique, mathématiques et statistiques
Anemone
[SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics
Classification
automated regional segmentation
C30 - Documentation et information
reproductive structures
Phénologie
Cartography
F40 - Écologie végétale
Physiologie de la reproduction
plant phenology
lcsh:Plant culture
010603 evolutionary biology
Visual data classification
03 medical and health sciences
[SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems
Hepatica
lcsh:SB1-1110
Détermination des espèces
Relative species abundance
030304 developmental biology
deep learning
15. Life on land
Traitement numérique d'image
biology.organism_classification
Trillium
Taxon
Herbarium
13. Climate action
Modélisation
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
Subjects
Details
- Language :
- English
- ISSN :
- 1664462X
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Frontiers in Plant Science
- Accession number :
- edsair.doi.dedup.....ca660ae8958ce07b5b748bb2e006c20b