↓ Skip to main content

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
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#36 of 1,127)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
2 news outlets
twitter
7 X users
patent
3 patents

Citations

dimensions_citation
375 Dimensions

Readers on

mendeley
743 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
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 743 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 743 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 139 19%
Student > Master 106 14%
Researcher 97 13%
Student > Bachelor 56 8%
Student > Doctoral Student 34 5%
Other 100 13%
Unknown 211 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 196 26%
Engineering 91 12%
Computer Science 75 10%
Environmental Science 33 4%
Earth and Planetary Sciences 20 3%
Other 77 10%
Unknown 251 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 13 April 2023.
All research outputs
#1,070,509
of 23,760,369 outputs
Outputs from Plant Methods
#36
of 1,127 outputs
Outputs of similar age
#23,406
of 325,672 outputs
Outputs of similar age from Plant Methods
#1
of 34 outputs
Altmetric has tracked 23,760,369 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,127 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one has done particularly well, scoring higher than 96% 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 325,672 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 92% of its contemporaries.
We're also able to compare this research output to 34 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.