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The use of plant models in deep learning: an application to leaf counting in rosette plants

Overview of attention for article published in Plant Methods, January 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
13 X users

Citations

dimensions_citation
203 Dimensions

Readers on

mendeley
259 Mendeley
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Title
The use of plant models in deep learning: an application to leaf counting in rosette plants
Published in
Plant Methods, January 2018
DOI 10.1186/s13007-018-0273-z
Pubmed ID
Authors

Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Ian Stavness

Abstract

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 259 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 259 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 21%
Student > Master 32 12%
Researcher 25 10%
Student > Doctoral Student 19 7%
Student > Bachelor 10 4%
Other 42 16%
Unknown 76 29%
Readers by discipline Count As %
Agricultural and Biological Sciences 60 23%
Computer Science 46 18%
Engineering 36 14%
Biochemistry, Genetics and Molecular Biology 6 2%
Unspecified 5 2%
Other 19 7%
Unknown 87 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 15 June 2021.
All research outputs
#2,075,302
of 23,571,271 outputs
Outputs from Plant Methods
#95
of 1,120 outputs
Outputs of similar age
#50,363
of 444,212 outputs
Outputs of similar age from Plant Methods
#2
of 29 outputs
Altmetric has tracked 23,571,271 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,120 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 91% 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 444,212 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 29 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 96% of its contemporaries.