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Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline

Overview of attention for article published in Plant Methods, July 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
19 tweeters

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
44 Mendeley
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Title
Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline
Published in
Plant Methods, July 2017
DOI 10.1186/s13007-017-0207-1
Pubmed ID
Authors

Lionel X. Dupuy, Gladys Wright, Jacqueline A. Thompson, Anna Taylor, Sebastien Dekeyser, Christopher P. White, William T. B. Thomas, Mark Nightingale, John P. Hammond, Neil S. Graham, Catherine L. Thomas, Martin R. Broadley, Philip J. White, Dupuy, Lionel X., Wright, Gladys, Thompson, Jacqueline A., Taylor, Anna, Dekeyser, Sebastien, White, Christopher P., Thomas, William T. B., Nightingale, Mark, Hammond, John P., Graham, Neil S., Thomas, Catherine L., Broadley, Martin R., White, Philip J.

Abstract

There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration. Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database. Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.

Twitter Demographics

The data shown below were collected from the profiles of 19 tweeters who shared this research output. Click here to find out more about how the information was compiled.

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 8 18%
Student > Ph. D. Student 7 16%
Student > Master 6 14%
Student > Doctoral Student 3 7%
Professor 3 7%
Other 6 14%
Unknown 11 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 59%
Arts and Humanities 1 2%
Biochemistry, Genetics and Molecular Biology 1 2%
Computer Science 1 2%
Earth and Planetary Sciences 1 2%
Other 2 5%
Unknown 12 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 20 July 2017.
All research outputs
#2,133,578
of 16,346,719 outputs
Outputs from Plant Methods
#112
of 798 outputs
Outputs of similar age
#50,470
of 269,422 outputs
Outputs of similar age from Plant Methods
#3
of 8 outputs
Altmetric has tracked 16,346,719 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 798 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has done well, scoring higher than 85% 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 269,422 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.