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Fine-grained weed recognition using Swin Transformer and two-stage transfer learning

Overview of attention for article published in Frontiers in Plant Science, March 2023
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Mentioned by

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1 X user

Citations

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5 Dimensions

Readers on

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12 Mendeley
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Title
Fine-grained weed recognition using Swin Transformer and two-stage transfer learning
Published in
Frontiers in Plant Science, March 2023
DOI 10.3389/fpls.2023.1134932
Pubmed ID
Authors

Yecheng Wang, Shuangqing Zhang, Baisheng Dai, Sensen Yang, Haochen Song

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

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 25%
Unspecified 1 8%
Student > Master 1 8%
Unknown 7 58%
Readers by discipline Count As %
Computer Science 2 17%
Agricultural and Biological Sciences 2 17%
Unspecified 1 8%
Psychology 1 8%
Unknown 6 50%
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 13 March 2023.
All research outputs
#20,897,310
of 23,524,722 outputs
Outputs from Frontiers in Plant Science
#17,404
of 21,545 outputs
Outputs of similar age
#268,030
of 340,401 outputs
Outputs of similar age from Frontiers in Plant Science
#1,018
of 1,031 outputs
Altmetric has tracked 23,524,722 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 21,545 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 340,401 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 1,031 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.