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Computer vision applied to herbarium specimens of German trees: testing the future utility of the millions of herbarium specimen images for automated identification

Overview of attention for article published in BMC Ecology and Evolution, November 2016
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Title
Computer vision applied to herbarium specimens of German trees: testing the future utility of the millions of herbarium specimen images for automated identification
Published in
BMC Ecology and Evolution, November 2016
DOI 10.1186/s12862-016-0827-5
Pubmed ID
Authors

Jakob Unger, Dorit Merhof, Susanne Renner

Abstract

Global Plants, a collaborative between JSTOR and some 300 herbaria, now contains about 2.48 million high-resolution images of plant specimens, a number that continues to grow, and collections that are digitizing their specimens at high resolution are allocating considerable recourses to the maintenance of computer hardware (e.g., servers) and to acquiring digital storage space. We here apply machine learning, specifically the training of a Support-Vector-Machine, to classify specimen images into categories, ideally at the species level, using the 26 most common tree species in Germany as a test case. We designed an analysis pipeline and classification system consisting of segmentation, normalization, feature extraction, and classification steps and evaluated the system in two test sets, one with 26 species, the other with 17, in each case using 10 images per species of plants collected between 1820 and 1995, which simulates the empirical situation that most named species are represented in herbaria and databases, such as JSTOR, by few specimens. We achieved 73.21% accuracy of species assignments in the larger test set, and 84.88% in the smaller test set. The results of this first application of a computer vision algorithm trained on images of herbarium specimens shows that despite the problem of overlapping leaves, leaf-architectural features can be used to categorize specimens to species with good accuracy. Computer vision is poised to play a significant role in future rapid identification at least for frequently collected genera or species in the European flora.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 30%
Student > Ph. D. Student 14 17%
Student > Master 10 12%
Student > Doctoral Student 4 5%
Student > Postgraduate 4 5%
Other 14 17%
Unknown 12 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 49%
Computer Science 12 14%
Environmental Science 7 8%
Engineering 3 4%
Nursing and Health Professions 1 1%
Other 4 5%
Unknown 15 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 24 July 2017.
All research outputs
#6,495,686
of 25,374,647 outputs
Outputs from BMC Ecology and Evolution
#1,439
of 3,714 outputs
Outputs of similar age
#84,035
of 288,244 outputs
Outputs of similar age from BMC Ecology and Evolution
#41
of 83 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 3,714 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 gotten more attention than average, scoring higher than 60% 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 288,244 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.