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Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

Overview of attention for article published in Plant Methods, October 2017
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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 (#46 of 943)
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
1 news outlet
twitter
13 tweeters
patent
1 patent

Citations

dimensions_citation
186 Dimensions

Readers on

mendeley
500 Mendeley
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Title
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress
Published in
Plant Methods, October 2017
DOI 10.1186/s13007-017-0233-z
Pubmed ID
Authors

Amy Lowe, Nicola Harrison, Andrew P French

Abstract

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into 'healthy and diseased plant classification' with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 500 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 500 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 109 22%
Student > Master 89 18%
Researcher 77 15%
Student > Bachelor 45 9%
Student > Doctoral Student 22 4%
Other 66 13%
Unknown 92 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 148 30%
Engineering 75 15%
Computer Science 61 12%
Environmental Science 25 5%
Earth and Planetary Sciences 17 3%
Other 46 9%
Unknown 128 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 22 January 2021.
All research outputs
#1,256,921
of 20,109,407 outputs
Outputs from Plant Methods
#46
of 943 outputs
Outputs of similar age
#28,664
of 294,813 outputs
Outputs of similar age from Plant Methods
#1
of 12 outputs
Altmetric has tracked 20,109,407 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 943 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 294,813 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 12 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.