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High-throughput phenotyping for crop improvement in the genomics era

Published on May 1, 2019in Plant Science3.785
· DOI :10.1016/j.plantsci.2019.01.007
Reyazul Rouf Mir3
Estimated H-index: 3
(Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir),
M. P. Reynolds30
Estimated H-index: 30
(CIMMYT: International Maize and Wheat Improvement Center)
+ 2 AuthorsMohd Ashraf Bhat2
Estimated H-index: 2
(Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir)
Source
Abstract
Abstract Tremendous progress has been made with continually expanding genomics technologies to unravel and understand crop genomes. However, the impact of genomics data on crop improvement is still far from satisfactory, in large part due to a lack of effective phenotypic data; our capacity to collect useful high quality phenotypic data lags behind the current capacity to generate high-throughput genomics data. Thus, the research bottleneck in plant sciences is shifting from genotyping to phenotyping. This article review the current status of efforts made in the last decade to systematically collect phenotypic data to alleviate this ‘phenomics bottlenecks’ by recording trait data through sophisticated non-invasive imaging, spectroscopy, image analysis, robotics, high-performance computing facilities and phenomics databases. These modern phenomics platforms and tools aim to record data on traits like plant development, architecture, plant photosynthesis, growth or biomass productivity, on hundreds to thousands of plants in a single day, as a phenomics revolution. It is believed that this revolution will provide plant scientists with the knowledge and tools necessary for unlocking information coded in plant genomes. Efforts have been also made to present the advances made in the last 10 years in phenomics platforms and their use in generating phenotypic data on different traits in several major crops including rice, wheat, barley, and maize. The article also highlights the need for phenomics databases and phenotypic data sharing for crop improvement. The phenomics data generated has been used to identify genes/QTL through QTL mapping, association mapping and genome-wide association studies (GWAS) for genomics-assisted breeding (GAB) for crop improvement.
  • References (114)
  • Citations (8)
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References114
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#1Daniel Reynolds (Norwich Research Park)H-Index: 5
#2Frédéric Baret (INRA: Institut national de la recherche agronomique)H-Index: 63
Last. François Tardieu (INRA: Institut national de la recherche agronomique)H-Index: 60
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Abstract Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant ...
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#1Matthew P. Reynolds (CIMMYT: International Maize and Wheat Improvement Center)H-Index: 55
#2Alistair J. D. Pask (CIMMYT: International Maize and Wheat Improvement Center)H-Index: 3
Last. Arun K. Joshi (CIMMYT: International Maize and Wheat Improvement Center)H-Index: 34
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To accelerate genetic gains in breeding, physiological trait (PT) characterization of candidate parents can help make more strategic crosses, increasing the probability of accumulating favorable alleles compared to crossing relatively uncharacterized lines. In this study, crosses were designed to complement “source” with “sink” traits, where at least one parent was selected for favorable expression of biomass and/or radiation use efficiency—source—and the other for sink-related traits like harve...
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#1Guillaume Lobet (UCL: Université catholique de Louvain)H-Index: 16
Image analysis has become a powerful technique for most plant scientists. In recent years dozens of image analysis tools have been published in plant science journals. These tools cover the full spectrum of plant scales, from single cells to organs and canopies. However, the field of plant image analysis remains in its infancy. It still has to overcome important challenges, such as the lack of robust validation practices or the absence of long-term support. In this Opinion article, I: (i) presen...
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With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize recombinant inbred line population (n=167) across 16 developmental stages using the automatic phenotyping platform. QTL mapping with a high-density genetic link...
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Hitherto, most quantitative trait loci (QTL) of maize growth and biomass yield have been identified for a single time point, usually the final harvest stage. Through this approach cumulative effects are detected, without considering genetic factors causing phase-specific differences in growth rates. To assess the genetics of growth dynamics, we employed automated non-invasive phenotyping to monitor the plant sizes of 252 diverse maize inbred lines at eleven different developmental time points. 5...
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'Summary' I. 'Introduction' 839 II. 'Phenotypic differences between lab- and field-grown plants' 839 III. 'The shoot environment' 841 IV. 'The root environment' 845 V. 'Effects of plant density' 847 VI. 'Consistency among species or genotypes in ranking between lab and field' 848 VII. 'Translation of lab results to the field' 849 VIII. 'Conclusions' 851 'Acknowledgements' 851 'Author contributions' 852 References 852 Appendix A1 854 Summary Plant biologists often grow plants in growth chambers o...
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Background Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observati...
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Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNN) in unmanned aerial vehicle (UAV) imagery....
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