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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives
|
|---|---|
| Published in |
Frontiers in Plant Science, June 2017
|
| DOI | 10.3389/fpls.2017.01111 |
| Pubmed ID | |
| Authors |
Guijun Yang, Jiangang Liu, Chunjiang Zhao, Zhenhong Li, Yanbo Huang, Haiyang Yu, Bo Xu, Xiaodong Yang, Dongmei Zhu, Xiaoyan Zhang, Ruyang Zhang, Haikuan Feng, Xiaoqing Zhao, Zhenhai Li, Heli Li, Hao Yang |
| Abstract |
Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 11% |
| Switzerland | 2 | 11% |
| South Africa | 1 | 5% |
| Brazil | 1 | 5% |
| Tunisia | 1 | 5% |
| Bolivia, Plurinational State of | 1 | 5% |
| United Arab Emirates | 1 | 5% |
| Tanzania, United Republic of | 1 | 5% |
| Egypt | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 8 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 15 | 79% |
| Scientists | 2 | 11% |
| Practitioners (doctors, other healthcare professionals) | 1 | 5% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 692 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 692 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 124 | 18% |
| Student > Ph. D. Student | 111 | 16% |
| Researcher | 81 | 12% |
| Student > Bachelor | 44 | 6% |
| Student > Doctoral Student | 33 | 5% |
| Other | 84 | 12% |
| Unknown | 215 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 202 | 29% |
| Engineering | 78 | 11% |
| Environmental Science | 45 | 7% |
| Computer Science | 31 | 4% |
| Earth and Planetary Sciences | 27 | 4% |
| Other | 46 | 7% |
| Unknown | 263 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 26 May 2019.
All research outputs
#2,795,866
of 26,369,011 outputs
Outputs from Frontiers in Plant Science
#1,214
of 25,145 outputs
Outputs of similar age
#49,105
of 333,309 outputs
Outputs of similar age from Frontiers in Plant Science
#39
of 554 outputs
Altmetric has tracked 26,369,011 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,145 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 95% 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 333,309 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 85% of its contemporaries.
We're also able to compare this research output to 554 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 92% of its contemporaries.