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A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

Overview of attention for article published in Plant Methods, November 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

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8 tweeters

Citations

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65 Dimensions

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Title
A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
Published in
Plant Methods, November 2017
DOI 10.1186/s13007-017-0246-7
Pubmed ID
Authors

Nicolas Brichet, Christian Fournier, Olivier Turc, Olivier Strauss, Simon Artzet, Christophe Pradal, Claude Welcker, François Tardieu, Llorenç Cabrera-Bosquet

Abstract

In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1-7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses. We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth. The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 21%
Researcher 17 20%
Student > Master 9 10%
Student > Doctoral Student 8 9%
Student > Bachelor 6 7%
Other 12 14%
Unknown 16 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 48%
Engineering 10 12%
Computer Science 5 6%
Biochemistry, Genetics and Molecular Biology 3 3%
Physics and Astronomy 2 2%
Other 7 8%
Unknown 18 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 10 November 2017.
All research outputs
#2,024,104
of 12,122,714 outputs
Outputs from Plant Methods
#98
of 459 outputs
Outputs of similar age
#69,004
of 285,270 outputs
Outputs of similar age from Plant Methods
#11
of 58 outputs
Altmetric has tracked 12,122,714 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 459 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 78% 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 285,270 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 75% of its contemporaries.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.