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Plant diseases and pests detection based on deep learning: a review

Overview of attention for article published in Plant Methods, February 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
74 Dimensions

Readers on

mendeley
328 Mendeley
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Title
Plant diseases and pests detection based on deep learning: a review
Published in
Plant Methods, February 2021
DOI 10.1186/s13007-021-00722-9
Pubmed ID
Authors

Jun Liu, Xuewei Wang

Twitter Demographics

The data shown below were collected from the profiles of 8 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 328 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 328 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 13%
Student > Master 31 9%
Researcher 26 8%
Student > Bachelor 23 7%
Unspecified 18 5%
Other 57 17%
Unknown 130 40%
Readers by discipline Count As %
Computer Science 73 22%
Engineering 39 12%
Agricultural and Biological Sciences 28 9%
Unspecified 21 6%
Biochemistry, Genetics and Molecular Biology 6 2%
Other 22 7%
Unknown 139 42%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 21 July 2021.
All research outputs
#6,402,714
of 21,479,159 outputs
Outputs from Plant Methods
#399
of 1,029 outputs
Outputs of similar age
#120,322
of 313,698 outputs
Outputs of similar age from Plant Methods
#1
of 1 outputs
Altmetric has tracked 21,479,159 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,029 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 gotten more attention than average, scoring higher than 60% 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 313,698 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them