High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Jin, X and Zarco-Tejada, P and Schmidhalter, U and Reynolds, M P and Hawkesford, M J and Varshney, R K and Yang, T and Nie, C and Li, Z and Ming, B and Xiao, Y and Xie, Y and Li, S (2020) High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geoscience and Remote Sensing Magazine. pp. 1-33. ISSN 2473-2397

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Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields.

Item Type: Article
Divisions: Research Program : Genetic Gains
Uncontrolled Keywords: Genomics, Food Security, Breeding, Climate Change
Subjects: Others > Plant Breeding
Others > Genetics and Genomics
Others > Climate Change
Others > Food Security
Depositing User: Mr Arun S
Date Deposited: 06 Sep 2020 09:04
Last Modified: 06 Sep 2020 11:48
URI: http://oar.icrisat.org/id/eprint/11602
Official URL: https://doi.org/10.1109/MGRS.2020.2998816
Acknowledgement: The study was supported by the National Key Research and Development Program of China (grant 2016YFD0300605), National Natural Science Foundation of China (grant 41601369), and the Young Talents Program of the Institute of Crop Science under the Chinese Academy of Agricultural Sciences (grant S2019YC04). Malcolm J. Hawkesford is supported by the Biotechnology and Biological Sciences Research Council of the United Kingdom for funding the Designing Future Wheat program (grant BB/P016855/1) and the United Kingdom Department for Environment, Food, and Rural Affairs for funding the Wheat Genetic Improvement Network (grant CH1090).
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