eprintid: 11602
rev_number: 16
eprint_status: archive
userid: 3170
dir: disk0/00/01/16/02
datestamp: 2020-09-06 09:04:04
lastmod: 2020-09-06 11:48:46
status_changed: 2020-09-06 09:04:04
type: article
metadata_visibility: show
creators_name: Jin, X
creators_name: Zarco-Tejada, P
creators_name: Schmidhalter, U
creators_name: Reynolds, M P
creators_name: Hawkesford, M J
creators_name: Varshney, R K
creators_name: Yang, T
creators_name: Nie, C
creators_name: Li, Z
creators_name: Ming, B
creators_name: Xiao, Y
creators_name: Xie, Y
creators_name: Li, S
icrisatcreators_name: Varshney, R K
affiliation: Crop Phenotypic Innovation Research group, Institute of Crop Science, Chinese Academy of Agriculture Sciences
affiliation: School of Agriculture and the Melbourne School of Engineering, University of Melbourne, Australia
affiliation: Technical University of Munich
affiliation: wheat physiology at the International Maize and Wheat Improvement Center
affiliation: Rothamsted Research, Harpenden, United Kingdom
affiliation: ICRISAT (Patancheru)
affiliation: Center for Crop Germplasm Resources at the Institute of Crop Sciences, Chinese Academy of Agriculture Sciences
affiliation: Institute of Crop Science, Chinese Academy of Agricultural Sciences
affiliation: National Engineering Research Center for Information Technology in Agriculture, Beijing
affiliation: Crop Cultivation and Physiological Innovation team at the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing
affiliation: China Agricultural University, Beijing
country: China
country: India
title: High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms
ispublished: pub
subjects: PLB1
subjects: s2.13
subjects: s2.8
subjects: s26
divisions: CRPS3
full_text_status: public
keywords: Genomics, Food Security, Breeding, Climate Change
note: 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).
abstract: 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.
date: 2020-07
date_type: published
publication: IEEE Geoscience and Remote Sensing Magazine
pagerange: 1-33
id_number: doi:10.1109/MGRS.2020.2998816
refereed: TRUE
issn: 2473-2397
official_url: https://doi.org/10.1109/MGRS.2020.2998816
related_url_url: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=10.1109%2FMGRS.2020.2998816&btnG=
related_url_type: pub
citation:   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     
document_url: http://oar.icrisat.org/11602/1/paper115.pdf