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Fine-Grained Grape Leaf Diseases Recognition Method Based on Improved Lightweight Attention Network

Overview of attention for article published in Frontiers in Plant Science, October 2021
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1 X user

Citations

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13 Dimensions

Readers on

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11 Mendeley
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Title
Fine-Grained Grape Leaf Diseases Recognition Method Based on Improved Lightweight Attention Network
Published in
Frontiers in Plant Science, October 2021
DOI 10.3389/fpls.2021.738042
Pubmed ID
Authors

Peng Wang, Tong Niu, Yanru Mao, Bin Liu, Shuqin Yang, Dongjian He, Qiang Gao

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X Demographics

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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 9%
Student > Doctoral Student 1 9%
Professor 1 9%
Student > Ph. D. Student 1 9%
Student > Master 1 9%
Other 2 18%
Unknown 4 36%
Readers by discipline Count As %
Computer Science 2 18%
Unspecified 1 9%
Agricultural and Biological Sciences 1 9%
Environmental Science 1 9%
Chemistry 1 9%
Other 1 9%
Unknown 4 36%
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 22 October 2021.
All research outputs
#20,710,927
of 23,310,485 outputs
Outputs from Frontiers in Plant Science
#17,019
of 21,146 outputs
Outputs of similar age
#361,372
of 439,966 outputs
Outputs of similar age from Frontiers in Plant Science
#653
of 886 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 21,146 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 1st percentile – i.e., 1% 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 439,966 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 886 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.