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Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize

Overview of attention for article published in Plant Methods, June 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#12 of 1,023)
  • High Attention Score compared to outputs of the same age (97th percentile)

Mentioned by

twitter
78 tweeters
facebook
2 Facebook pages

Citations

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199 Dimensions

Readers on

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363 Mendeley
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Title
Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize
Published in
Plant Methods, June 2015
DOI 10.1186/s13007-015-0078-2
Pubmed ID
Authors

M Zaman-Allah, O Vergara, J L Araus, A Tarekegne, C Magorokosho, P J Zarco-Tejada, A Hornero, A Hernández Albà, B Das, P Craufurd, M Olsen, B M Prasanna, J Cairns

Abstract

Recent developments in unmanned aerial platforms (UAP) have provided research opportunities in assessing land allocation and crop physiological traits, including response to abiotic and biotic stresses. UAP-based remote sensing can be used to rapidly and cost-effectively phenotype large numbers of plots and field trials in a dynamic way using time series. This is anticipated to have tremendous implications for progress in crop genetic improvement. We present the use of a UAP equipped with sensors for multispectral imaging in spatial field variability assessment and phenotyping for low-nitrogen (low-N) stress tolerance in maize. Multispectral aerial images were used to (1) characterize experimental fields for spatial soil-nitrogen variability and (2) derive indices for crop performance under low-N stress. Overall, results showed that the aerial platform enables to effectively characterize spatial field variation and assess crop performance under low-N stress. The Normalized Difference Vegetation Index (NDVI) data derived from spectral imaging presented a strong correlation with ground-measured NDVI, crop senescence index and grain yield. This work suggests that the aerial sensing platform designed for phenotyping studies has the potential to effectively assist in crop genetic improvement against abiotic stresses like low-N provided that sensors have enough resolution for plot level data collection. Limitations and future potential uses are also discussed.

Twitter Demographics

The data shown below were collected from the profiles of 78 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 363 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 3 <1%
Colombia 1 <1%
Australia 1 <1%
Brazil 1 <1%
Indonesia 1 <1%
Canada 1 <1%
Benin 1 <1%
Mexico 1 <1%
Czechia 1 <1%
Other 4 1%
Unknown 348 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 77 21%
Student > Ph. D. Student 70 19%
Student > Master 60 17%
Student > Bachelor 28 8%
Student > Doctoral Student 21 6%
Other 57 16%
Unknown 50 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 156 43%
Engineering 47 13%
Environmental Science 27 7%
Earth and Planetary Sciences 23 6%
Computer Science 15 4%
Other 24 7%
Unknown 71 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 58. 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 30 March 2017.
All research outputs
#560,485
of 21,397,370 outputs
Outputs from Plant Methods
#12
of 1,023 outputs
Outputs of similar age
#7,239
of 246,482 outputs
Outputs of similar age from Plant Methods
#1
of 1 outputs
Altmetric has tracked 21,397,370 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,023 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done particularly well, scoring higher than 98% 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 246,482 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them