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Non-destructive Phenotypic Analysis of Early Stage Tree Seedling Growth Using an Automated Stereovision Imaging Method

Overview of attention for article published in Frontiers in Plant Science, October 2016
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Title
Non-destructive Phenotypic Analysis of Early Stage Tree Seedling Growth Using an Automated Stereovision Imaging Method
Published in
Frontiers in Plant Science, October 2016
DOI 10.3389/fpls.2016.01644
Pubmed ID
Authors

Antonio Montagnoli, Mattia Terzaghi, Nicoletta Fulgaro, Borys Stoew, Jan Wipenmyr, Dag Ilver, Cristina Rusu, Gabriella S. Scippa, Donato Chiatante

Abstract

A plant phenotyping approach was applied to evaluate growth rate of containerized tree seedlings during the precultivation phase following seed germination. A simple and affordable stereo optical system was used to collect stereoscopic red-green-blue (RGB) images of seedlings at regular intervals of time. Comparative analysis of these images by means of a newly developed software enabled us to calculate (a) the increments of seedlings height and (b) the percentage greenness of seedling leaves. Comparison of these parameters with destructive biomass measurements showed that the height traits can be used to estimate seedling growth for needle-leaved plant species whereas the greenness trait can be used for broad-leaved plant species. Despite the need to adjust for plant type, growth stage and light conditions this new, cheap, rapid, and sustainable phenotyping approach can be used to study large-scale phenome variations due to genome variability and interaction with environmental factors.

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The data shown below were collected from the profile of 1 X user 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 23%
Student > Ph. D. Student 6 14%
Professor 5 11%
Other 4 9%
Student > Master 4 9%
Other 5 11%
Unknown 10 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 45%
Environmental Science 3 7%
Biochemistry, Genetics and Molecular Biology 2 5%
Engineering 2 5%
Computer Science 1 2%
Other 3 7%
Unknown 13 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 17 January 2017.
All research outputs
#20,438,056
of 25,117,541 outputs
Outputs from Frontiers in Plant Science
#16,302
of 24,077 outputs
Outputs of similar age
#245,540
of 320,971 outputs
Outputs of similar age from Frontiers in Plant Science
#266
of 420 outputs
Altmetric has tracked 25,117,541 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 24,077 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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 320,971 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 420 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.