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Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Overview of attention for article published in Plant Methods, December 2015
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35 Mendeley
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Title
Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis
Published in
Plant Methods, December 2015
DOI 10.1186/s13007-015-0100-8
Pubmed ID
Authors

Bo Li, Michelle T. Hulin, Philip Brain, John W. Mansfield, Robert W. Jackson, Richard J. Harrison

Abstract

Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance. In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study. Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 20%
Researcher 4 11%
Student > Master 3 9%
Professor 2 6%
Other 2 6%
Other 5 14%
Unknown 12 34%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 31%
Biochemistry, Genetics and Molecular Biology 2 6%
Computer Science 2 6%
Environmental Science 1 3%
Unspecified 1 3%
Other 3 9%
Unknown 15 43%
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 26 December 2015.
All research outputs
#15,352,477
of 22,836,570 outputs
Outputs from Plant Methods
#830
of 1,082 outputs
Outputs of similar age
#228,875
of 390,633 outputs
Outputs of similar age from Plant Methods
#16
of 18 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,082 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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 390,633 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.