TY  - JOUR
N2  - 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.
N1  - 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).
AV  - public
KW  - Genomics
KW  -  Food Security
KW  -  Breeding
KW  -  Climate Change
A1  - Jin, X
A1  - Zarco-Tejada, P
A1  - Schmidhalter, U
A1  - Reynolds, M P
A1  - Hawkesford, M J
A1  - Varshney, R K
A1  - Yang, T
A1  - Nie, C
A1  - Li, Z
A1  - Ming, B
A1  - Xiao, Y
A1  - Xie, Y
A1  - Li, S
TI  - High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms
UR  - https://doi.org/10.1109/MGRS.2020.2998816
JF  - IEEE Geoscience and Remote Sensing Magazine
SP  - 1
Y1  - 2020/07//
ID  - icrisat11602
EP  - 33
SN  - 2473-2397
ER  -