Title |
High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis
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Published in |
Plant Methods, June 2013
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DOI | 10.1186/1746-4811-9-17 |
Pubmed ID | |
Authors |
Céline Rousseau, Etienne Belin, Edouard Bove, David Rousseau, Frédéric Fabre, Romain Berruyer, Jacky Guillaumès, Charles Manceau, Marie-Agnès Jacques, Tristan Boureau |
Abstract |
In order to select for quantitative plant resistance to pathogens, high throughput approaches that can precisely quantify disease severity are needed. Automation and use of calibrated image analysis should provide more accurate, objective and faster analyses than visual assessments. In contrast to conventional visible imaging, chlorophyll fluorescence imaging is not sensitive to environmental light variations and provides single-channel images prone to a segmentation analysis by simple thresholding approaches. Among the various parameters used in chlorophyll fluorescence imaging, the maximum quantum yield of photosystem II photochemistry (Fv/Fm) is well adapted to phenotyping disease severity. Fv/Fm is an indicator of plant stress that displays a robust contrast between infected and healthy tissues. In the present paper, we aimed at the segmentation of Fv/Fm images to quantify disease severity. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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France | 3 | 1% |
Brazil | 3 | 1% |
Germany | 2 | <1% |
Spain | 2 | <1% |
Malaysia | 1 | <1% |
United Kingdom | 1 | <1% |
New Zealand | 1 | <1% |
Chile | 1 | <1% |
Other | 2 | <1% |
Unknown | 228 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 67 | 27% |
Researcher | 51 | 21% |
Student > Master | 34 | 14% |
Student > Doctoral Student | 20 | 8% |
Professor | 11 | 4% |
Other | 32 | 13% |
Unknown | 32 | 13% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 17 | 7% |
Engineering | 13 | 5% |
Computer Science | 9 | 4% |
Environmental Science | 8 | 3% |
Other | 10 | 4% |
Unknown | 41 | 17% |