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High-throughput phenotyping for crop improvement in the genomics era
Journal article   Peer reviewed

High-throughput phenotyping for crop improvement in the genomics era

Reyazul Rouf Mir, Mathew Reynolds, Francisco Pinto, Mohd Anwar Khan and Mohd Ashraf Bhat
Plant science (Limerick), Vol.282, pp.60-72
2019
PMID: 31003612

Abstract

Biochemistry & Molecular Biology Life Sciences & Biomedicine Plant Sciences Science & Technology
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 modem 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.

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Source: InCites

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.169 Remote Sensing
4.169.91 Vegetation Mapping
Web Of Science research areas
Biochemistry & Molecular Biology
Plant Sciences
ESI research areas
Plant & Animal Science
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