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Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets

Overview of attention for article published in Plant Methods, April 2015
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Title
Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets
Published in
Plant Methods, April 2015
DOI 10.1186/s13007-015-0068-4
Pubmed ID
Authors

Céline Rousseau, Gilles Hunault, Sylvain Gaillard, Julie Bourbeillon, Gregory Montiel, Philippe Simier, Claire Campion, Marie-Agnès Jacques, Etienne Belin, Tristan Boureau

Abstract

Image analysis is increasingly used in plant phenotyping. Among the various imaging techniques that can be used in plant phenotyping, chlorophyll fluorescence imaging allows imaging of the impact of biotic or abiotic stresses on leaves. Numerous chlorophyll fluorescence parameters may be measured or calculated, but only a few can produce a contrast in a given condition. Therefore, automated procedures that help screening chlorophyll fluorescence image datasets are needed, especially in the perspective of high-throughput plant phenotyping. We developed an automatic procedure aiming at facilitating the identification of chlorophyll fluorescence parameters impacted on leaves by a stress. First, for each chlorophyll fluorescence parameter, the procedure provides an overview of the data by automatically creating contact sheets of images and/or histograms. Such contact sheets enable a fast comparison of the impact on leaves of various treatments, or of the contrast dynamics during the experiments. Second, based on the global intensity of each chlorophyll fluorescence parameter, the procedure automatically produces radial plots and box plots allowing the user to identify chlorophyll fluorescence parameters that discriminate between treatments. Moreover, basic statistical analysis is automatically generated. Third, for each chlorophyll fluorescence parameter the procedure automatically performs a clustering analysis based on the histograms. This analysis clusters images of plants according to their health status. We applied this procedure to monitor the impact of the inoculation of the root parasitic plant Phelipanche ramosa on Arabidopsis thaliana ecotypes Col-0 and Ler. Using this automatic procedure, we identified eight chlorophyll fluorescence parameters discriminating between the two ecotypes of A. thaliana, and five impacted by the infection of Arabidopsis thaliana by P. ramosa. More generally, this procedure may help to identify chlorophyll fluorescence parameters impacted by various types of stresses. We implemented this procedure at http://www.phenoplant.org freely accessible to users of the plant phenotyping community.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 2 3%
Argentina 1 1%
Brazil 1 1%
Spain 1 1%
Japan 1 1%
Unknown 65 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 24%
Student > Ph. D. Student 14 20%
Student > Master 9 13%
Student > Doctoral Student 5 7%
Professor > Associate Professor 5 7%
Other 13 18%
Unknown 8 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 61%
Biochemistry, Genetics and Molecular Biology 5 7%
Computer Science 2 3%
Mathematics 1 1%
Arts and Humanities 1 1%
Other 3 4%
Unknown 16 23%
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 14 April 2015.
All research outputs
#20,273,512
of 22,805,349 outputs
Outputs from Plant Methods
#1,048
of 1,080 outputs
Outputs of similar age
#223,611
of 264,201 outputs
Outputs of similar age from Plant Methods
#14
of 16 outputs
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