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Fruit Detection and Pose Estimation for Grape Cluster–Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks

Overview of attention for article published in Frontiers in Robotics and AI, June 2021
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Mentioned by

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1 X user

Citations

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34 Dimensions

Readers on

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28 Mendeley
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Title
Fruit Detection and Pose Estimation for Grape Cluster–Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks
Published in
Frontiers in Robotics and AI, June 2021
DOI 10.3389/frobt.2021.626989
Pubmed ID
Authors

Wei Yin, Hanjin Wen, Zhengtong Ning, Jian Ye, Zhiqiang Dong, Lufeng Luo

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 11%
Researcher 2 7%
Lecturer 1 4%
Student > Doctoral Student 1 4%
Student > Bachelor 1 4%
Other 3 11%
Unknown 17 61%
Readers by discipline Count As %
Engineering 4 14%
Computer Science 2 7%
Agricultural and Biological Sciences 2 7%
Unspecified 1 4%
Unknown 19 68%
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 June 2021.
All research outputs
#18,807,229
of 23,308,124 outputs
Outputs from Frontiers in Robotics and AI
#1,331
of 1,540 outputs
Outputs of similar age
#319,491
of 443,946 outputs
Outputs of similar age from Frontiers in Robotics and AI
#115
of 129 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,540 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. 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 443,946 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 129 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.