Literature DB >> 28787611

Plant Phenomics, From Sensors to Knowledge.

François Tardieu1, Llorenç Cabrera-Bosquet2, Tony Pridmore3, Malcolm Bennett4.   

Abstract

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2017        PMID: 28787611     DOI: 10.1016/j.cub.2017.05.055

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  98 in total

1.  Using problem formulation to clarify the meaning of weight of evidence and biological relevance in environmental risk assessments for genetically modified crops.

Authors:  Alan Raybould; Karen Holt; Ian Kimber
Journal:  GM Crops Food       Date:  2019-06-11       Impact factor: 3.074

2.  Problem formulation and phenotypic characterisation for the development of novel crops.

Authors:  Alan Raybould
Journal:  Transgenic Res       Date:  2019-08       Impact factor: 2.788

Review 3.  Omics resources and omics-enabled approaches for achieving high productivity and improved quality in pea (Pisum sativum L.).

Authors:  Arun K Pandey; Diego Rubiales; Yonggang Wang; Pingping Fang; Ting Sun; Na Liu; Pei Xu
Journal:  Theor Appl Genet       Date:  2021-01-12       Impact factor: 5.699

Review 4.  Two decades of functional-structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology.

Authors:  Gaëtan Louarn; Youhong Song
Journal:  Ann Bot       Date:  2020-09-14       Impact factor: 4.357

5.  Functional Principal Component Analysis: A Robust Method for Time-Series Phenotypic Data.

Authors:  Yunqing Yu
Journal:  Plant Physiol       Date:  2020-08       Impact factor: 8.340

6.  Sustainable bioenergy for climate mitigation: developing drought-tolerant trees and grasses.

Authors:  G Taylor; I S Donnison; D Murphy-Bokern; M Morgante; M-B Bogeat-Triboulot; R Bhalerao; M Hertzberg; A Polle; A Harfouche; F Alasia; V Petoussi; D Trebbi; K Schwarz; J J B Keurentjes; M Centritto; B Genty; J Flexas; E Grill; S Salvi; W J Davies
Journal:  Ann Bot       Date:  2019-10-29       Impact factor: 4.357

Review 7.  Accelerating crop genetic gains with genomic selection.

Authors:  Kai Peter Voss-Fels; Mark Cooper; Ben John Hayes
Journal:  Theor Appl Genet       Date:  2018-12-19       Impact factor: 5.699

Review 8.  Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.

Authors:  Jacob I Marsh; Haifei Hu; Mitchell Gill; Jacqueline Batley; David Edwards
Journal:  Theor Appl Genet       Date:  2021-04-14       Impact factor: 5.699

Review 9.  Plant multiscale networks: charting plant connectivity by multi-level analysis and imaging techniques.

Authors:  Xi Zhang; Yi Man; Xiaohong Zhuang; Jinbo Shen; Yi Zhang; Yaning Cui; Meng Yu; Jingjing Xing; Guangchao Wang; Na Lian; Zijian Hu; Lingyu Ma; Weiwei Shen; Shunyao Yang; Huimin Xu; Jiahui Bian; Yanping Jing; Xiaojuan Li; Ruili Li; Tonglin Mao; Yuling Jiao; Haiyun Ren; Jinxing Lin
Journal:  Sci China Life Sci       Date:  2021-03-12       Impact factor: 6.038

10.  Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry.

Authors:  Laura M Zingaretti; Amparo Monfort; Miguel Pérez-Enciso
Journal:  Plant Phenomics       Date:  2021-05-12
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