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Going deeper in the automated identification of Herbarium specimens

Overview of attention for article published in BMC Ecology and Evolution, August 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
6 news outlets
blogs
3 blogs
twitter
61 X users
facebook
2 Facebook pages
reddit
1 Redditor

Citations

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179 Dimensions

Readers on

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272 Mendeley
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Title
Going deeper in the automated identification of Herbarium specimens
Published in
BMC Ecology and Evolution, August 2017
DOI 10.1186/s12862-017-1014-z
Pubmed ID
Authors

Jose Carranza-Rojas, Herve Goeau, Pierre Bonnet, Erick Mata-Montero, Alexis Joly

Abstract

Hundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field. In this work, we propose to study and evaluate the accuracy with which herbarium images can be potentially exploited for species identification with deep learning technology. In addition, we propose to study if the combination of herbarium sheets with photos of plants in the field is relevant in terms of accuracy, and finally, we explore if herbarium images from one region that has one specific flora can be used to do transfer learning to another region with other species; for example, on a region under-represented in terms of collected data. This is, to our knowledge, the first study that uses deep learning to analyze a big dataset with thousands of species from herbaria. Results show the potential of Deep Learning on herbarium species identification, particularly by training and testing across different datasets from different herbaria. This could potentially lead to the creation of a semi, or even fully automated system to help taxonomists and experts with their annotation, classification, and revision works.

X Demographics

X Demographics

The data shown below were collected from the profiles of 61 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 272 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 272 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 59 22%
Student > Ph. D. Student 31 11%
Student > Master 28 10%
Student > Bachelor 19 7%
Other 18 7%
Other 52 19%
Unknown 65 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 93 34%
Computer Science 34 13%
Environmental Science 20 7%
Engineering 12 4%
Biochemistry, Genetics and Molecular Biology 8 3%
Other 27 10%
Unknown 78 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 96. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 February 2018.
All research outputs
#443,733
of 25,571,620 outputs
Outputs from BMC Ecology and Evolution
#92
of 3,717 outputs
Outputs of similar age
#9,365
of 328,625 outputs
Outputs of similar age from BMC Ecology and Evolution
#3
of 67 outputs
Altmetric has tracked 25,571,620 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,717 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,625 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.