The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives
Open accessAbstract
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.
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Cites
Yield trends are insufficient to double global crop production by 2050
Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066428 · Cited by 3,415
Searching for intellectual turning points: progressive knowledge domain visualization
Chen, C. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307513100 · Cited by 2,791
Field high-throughput phenotyping: the new crop breeding frontier
Araus, J. L., & Cairns, J. E. (2014) · Trends in Plant Science · doi:10.1016/j.tplants.2013.09.008 · Cited by 1,817 · cited 6×
“…and biochemical characteristics and traits that are influenced by the genetic information of the crop and environmental factors, and it can be divided into different levels, such as groups, individuals, organs, tissues and cells (Cobb et al., 2013; Yang et al., 2013; Araus and Cairns, 2014).”
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Cited by
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges
Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., & Guizani, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909530 · Cited by 2,529
Maimaitijiang, M., Sagan, V., Sidike, P., Maimaitiyiming, M., Hartling, S., Peterson, K. T., Maw, M. J. W., Shakoor, N., Mockler, T., & Fritschi, F. B. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.03.003 · Cited by 253
High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery
Carrasco-Escobar, G., Manrique, E., Ruiz-Cabrejos, J., Saavedra, M., Alava, F., Bickersmith, S., Prussing, C., Vinetz, J. M., Conn, J. E., Moreno, M., et al. (2019) · PLOS Neglected Tropical Diseases · doi:10.1371/journal.pntd.0007105 · Cited by 126
Metadata
- Authors
- Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R., Feng, H., Zhao, X., Li, Z., Li, H., & Yang, H.
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2017-06-30
- Volume
- 8
- Pages
- 1111-1111
- ISSN
- 1664-462X (ISSN-L)
- OpenAlex
- W2728224506
- Cited by
- 767
- References
- 3
- FWCI
- 40.68
- Type
- journal article
- Language
- en
- Open access
- gold
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- Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R., Feng, H., Zhao, X., Li, Z., Li, H., & Yang, H. (2017). Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2017.01111
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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