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RhizoChamber-Monitor: a robotic platform and software enabling characterization of root growth

Overview of attention for article published in Plant Methods, June 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#39 of 934)
  • High Attention Score compared to outputs of the same age (90th percentile)

Mentioned by

twitter
37 tweeters

Citations

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15 Dimensions

Readers on

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42 Mendeley
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Title
RhizoChamber-Monitor: a robotic platform and software enabling characterization of root growth
Published in
Plant Methods, June 2018
DOI 10.1186/s13007-018-0316-5
Pubmed ID
Authors

Jie Wu, Qian Wu, Loïc Pagès, Yeqing Yuan, Xiaolei Zhang, Mingwei Du, Xiaoli Tian, Zhaohu Li

Abstract

In order to efficiently determine genotypic differences in rooting patterns of crops, novel hardware and software are needed simultaneously to characterize dynamics of root development. We describe a prototype robotic monitoring platform-the RhizoChamber-Monitor for analyzing growth patterns of plant roots automatically. The RhizoChamber-Monitor comprises an automatic imaging system for acquiring sequential images of roots which grow on a cloth substrate in custom rhizoboxes, an automatic irrigation system and a flexible shading arrangement. A customized image processing software was developed to analyze the spatio-temporal dynamics of root growth from time-course images of multiple plants. This software can quantify overall growth of roots and extract detailed growth traits (e.g. dynamics of length and diameter) of primary roots and of individual lateral roots automatically. It can also identify local growth traits of lateral roots (pseudo-mean-length and pseudo-maximum-length) semi-automatically. Two cotton genotypes were used to test both the physical platform and the analysis software. The combination of hardware and software is expected to facilitate quantification of root geometry and its spatio-temporal growth patterns, and therefore to provide opportunities for high-throughput root phenotyping in support of crop breeding to optimize root architecture.

Twitter Demographics

The data shown below were collected from the profiles of 37 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 26%
Researcher 9 21%
Professor > Associate Professor 4 10%
Student > Master 3 7%
Student > Doctoral Student 3 7%
Other 7 17%
Unknown 5 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 50%
Engineering 5 12%
Computer Science 3 7%
Environmental Science 2 5%
Earth and Planetary Sciences 2 5%
Other 3 7%
Unknown 6 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 03 December 2021.
All research outputs
#1,069,098
of 19,870,288 outputs
Outputs from Plant Methods
#39
of 934 outputs
Outputs of similar age
#27,079
of 294,741 outputs
Outputs of similar age from Plant Methods
#1
of 1 outputs
Altmetric has tracked 19,870,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 934 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done particularly well, scoring higher than 95% 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 294,741 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 90% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them