↓ Skip to main content

Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis

Overview of attention for article published in Plant Methods, January 2018
Altmetric Badge

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
30 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis
Published in
Plant Methods, January 2018
DOI 10.1186/s13007-018-0272-0
Pubmed ID
Authors

Danny Awty-Carroll, John Clifton-Brown, Paul Robson

Abstract

Miscanthus is a leading second generation bio-energy crop. It is mostly rhizome propagated; however, the increasing use of seed is resulting in a greater need to investigate germination. Miscanthus seed are small, germination is often poor and carried out without sterilisation; therefore, automated methods applied to germination detection must be able to cope with, for example, thresholding of small objects, low germination frequency and the presence or absence of mould. Machine learning using k-NN improved the scoring of different phenotypes encountered in Miscanthus seed. The k-NN-based algorithm was effective in scoring the germination of seed images when compared with human scores of the same images. The trueness of the k-NN result was 0.69-0.7, as measured using the area under a ROC curve. When the k-NN classifier was tested on an optimised image subset of seed an area under the ROC curve of 0.89 was achieved. The method compared favourably to an established technique. With non-ideal seed images that included mould and broken seed the k-NN classifier was less consistent with human assessments. The most accurate assessment of germination with which to train classifiers is difficult to determine but the k-NN classifier provided an impartial consistent measurement of this important trait. It was more reproducible than the existing human scoring methods and was demonstrated to give a high degree of trueness to the human score.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 20%
Researcher 4 13%
Student > Master 3 10%
Professor 3 10%
Student > Doctoral Student 2 7%
Other 4 13%
Unknown 8 27%
Readers by discipline Count As %
Computer Science 6 20%
Agricultural and Biological Sciences 6 20%
Engineering 3 10%
Social Sciences 2 7%
Arts and Humanities 1 3%
Other 4 13%
Unknown 8 27%

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 09 June 2020.
All research outputs
#13,791,086
of 17,972,612 outputs
Outputs from Plant Methods
#713
of 864 outputs
Outputs of similar age
#254,886
of 377,201 outputs
Outputs of similar age from Plant Methods
#3
of 3 outputs
Altmetric has tracked 17,972,612 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 864 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 12th percentile – i.e., 12% 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 377,201 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.