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Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging

Overview of attention for article published in Plant Methods, March 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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1 news outlet
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2 X users

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243 Mendeley
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Title
Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging
Published in
Plant Methods, March 2018
DOI 10.1186/s13007-018-0287-6
Pubmed ID
Authors

Zohaib Khan, Vahid Rahimi-Eichi, Stephan Haefele, Trevor Garnett, Stanley J. Miklavcic

Abstract

Unmanned aerial vehicles offer the opportunity for precision agriculture to efficiently monitor agricultural land. A vegetation index (VI) derived from an aerially observed multispectral image (MSI) can quantify crop health, moisture and nutrient content. However, due to the high cost of multispectral sensors, alternate, low-cost solutions have lately received great interest. We present a novel method for model-based estimation of a VI using RGB color images. The non-linear spatio-spectral relationship between the RGB image of vegetation and the index computed by its corresponding MSI is learned through deep neural networks. The learned models can be used to estimate VI of a crop segment. Analysis of images obtained in wheat breeding trials show that the aerially observed VI was highly correlated with ground-measured VI. In addition, VI estimates based on RGB images were highly correlated with VI deduced from MSIs. Spatial, spectral and temporal information of images contributed to estimation of VI. Both intra-variety and inter-variety differences were preserved by estimated VI. However, VI estimates were reliable until just before significant appearance of senescence. The proposed approach validates that it is reasonable to accurately estimate VI using deep neural networks. The results prove that RGB images contain sufficient information for VI estimation. It demonstrates that low-cost VI measurement is possible with standard RGB cameras.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 243 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 243 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 38 16%
Student > Ph. D. Student 37 15%
Researcher 33 14%
Student > Bachelor 20 8%
Student > Doctoral Student 12 5%
Other 38 16%
Unknown 65 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 76 31%
Engineering 28 12%
Computer Science 27 11%
Environmental Science 12 5%
Earth and Planetary Sciences 6 2%
Other 18 7%
Unknown 76 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 14 May 2018.
All research outputs
#3,121,756
of 23,026,672 outputs
Outputs from Plant Methods
#153
of 1,089 outputs
Outputs of similar age
#66,305
of 333,763 outputs
Outputs of similar age from Plant Methods
#3
of 25 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,089 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 85% 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 333,763 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 80% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.