Plant Phenomics, From Sensors to Knowledge

被引:351
作者
Tardieu, Francois [1 ]
Cabrera-Bosquet, Llorenc [1 ]
Pridmore, Tony [2 ]
Bennett, Malcolm [3 ]
机构
[1] INRA, Lab Ecophysiol Plantes Stress Environm, F-34060 Montpellier, France
[2] Univ Nottingham, Sch Comp Sci, Nottingham NG8 1BB, England
[3] Univ Nottingham, Sch Biosci, Plant & Crop Sci, Nottingham LE12 3RD, England
基金
英国生物技术与生命科学研究理事会;
关键词
QUANTITATIVE TRAIT LOCI; SOIL-WATER DEFICIT; ARABIDOPSIS-THALIANA; LEAF GROWTH; ENVIRONMENT INTERACTION; HYDRAULIC CONDUCTANCE; AUTOMATED RECOVERY; EVAPORATIVE DEMAND; INFORMATION-SYSTEM; GENETIC-VARIATION;
D O I
10.1016/j.cub.2017.05.055
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:R770 / R783
页数:14
相关论文
共 121 条
[51]   Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping [J].
Klukas, Christian ;
Chen, Dijun ;
Pape, Jean-Michel .
PLANT PHYSIOLOGY, 2014, 165 (02) :506-518
[52]  
Korhonen L, 2009, FOREST SCI, V55, P323
[53]   Towards recommendations for metadata and data handling in plant phenotyping [J].
Krajewski, Pawel ;
Chen, Dijun ;
Cwiek, Hanna ;
van Dijk, Aalt D. J. ;
Fiorani, Fabio ;
Kersey, Paul ;
Klukas, Christian ;
Lange, Matthias ;
Markiewicz, Augustyn ;
Nap, Jan Peter ;
van Oeveren, Jan ;
Pommier, Cyril ;
Scholz, Uwe ;
van Schriek, Marco ;
Usadel, Bjoern ;
Weise, Stephan .
JOURNAL OF EXPERIMENTAL BOTANY, 2015, 66 (18) :5417-5427
[54]   ImageNet Classification with Deep Convolutional Neural Networks [J].
Krizhevsky, Alex ;
Sutskever, Ilya ;
Hinton, Geoffrey E. .
COMMUNICATIONS OF THE ACM, 2017, 60 (06) :84-90
[55]   Generation of Leaf Shape Through Early Patterns of Growth and Tissue Polarity [J].
Kuchen, Erika E. ;
Fox, Samantha ;
de Reuille, Pierre Barbier ;
Kennaway, Richard ;
Bensmihen, Sandra ;
Avondo, Jerome ;
Calder, Grant M. ;
Southam, Paul ;
Robinson, Sarah ;
Bangham, Andrew ;
Coen, Enrico .
SCIENCE, 2012, 335 (6072) :1092-1096
[56]   Making the most of 'omics' for crop breeding [J].
Langridge, Peter ;
Fleury, Delphine .
TRENDS IN BIOTECHNOLOGY, 2011, 29 (01) :33-40
[57]   Dynamic imaging of cytosolic zinc in Arabidopsis roots combining FRET sensors and RootChip technology [J].
Lanquar, Viviane ;
Grossmann, Guido ;
Vinkenborg, Jan L. ;
Merkx, Maarten ;
Thomine, Sebastien ;
Frommer, Wolf B. .
NEW PHYTOLOGIST, 2014, 202 (01) :198-208
[58]   A fluorescent hormone biosensor reveals the dynamics of jasmonate signalling in plants [J].
Larrieu, Antoine ;
Champion, Antony ;
Legrand, Jonathan ;
Lavenus, Julien ;
Mast, David ;
Brunoud, Geraldine ;
Oh, Jaesung ;
Guyomarc'h, Soazig ;
Pizot, Maxime ;
Farmer, Edward E. ;
Turnbull, Colin ;
Vernoux, Teva ;
Bennett, Malcolm J. ;
Laplaze, Laurent .
Nature Communications, 2015, 6
[59]   A Review of Imaging Techniques for Plant Phenotyping [J].
Li, Lei ;
Zhang, Qin ;
Huang, Danfeng .
SENSORS, 2014, 14 (11) :20078-20111
[60]   An ontology-centric architecture for extensible scientific data management systems [J].
Li, Yuan-Fang ;
Kennedy, Gavin ;
Ngoran, Faith ;
Wu, Philip ;
Hunter, Jane .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2013, 29 (02) :641-653