Uncovering the hidden half of plants using new advances in root phenotyping

Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e., its structure and function) represents a major bot...

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Main Authors: Atkinson, Jonathan A., Pound, Michael P., Bennett, Malcolm J., Wells, Darren M.
Format: Article
Published: Elsevier 2019
Online Access:https://eprints.nottingham.ac.uk/52603/
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author Atkinson, Jonathan A.
Pound, Michael P.
Bennett, Malcolm J.
Wells, Darren M.
author_facet Atkinson, Jonathan A.
Pound, Michael P.
Bennett, Malcolm J.
Wells, Darren M.
author_sort Atkinson, Jonathan A.
building Nottingham Research Data Repository
collection Online Access
description Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e., its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting ‘big-data’ is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts.
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spelling nottingham-526032020-05-04T19:50:14Z https://eprints.nottingham.ac.uk/52603/ Uncovering the hidden half of plants using new advances in root phenotyping Atkinson, Jonathan A. Pound, Michael P. Bennett, Malcolm J. Wells, Darren M. Major increases in crop yield are required to keep pace with population growth and climate change. Improvements to the architecture of crop roots promise to deliver increases in water and nutrient use efficiency but profiling the root phenome (i.e., its structure and function) represents a major bottleneck. We describe how advances in imaging and sensor technologies are making root phenomic studies possible. However, methodological advances in acquisition, handling and processing of the resulting ‘big-data’ is becoming increasingly important. Advances in automated image analysis approaches such as Deep Learning promise to transform the root phenotyping landscape. Collectively, these innovations are helping drive the selection of the next-generation of crops to deliver real world impact for ongoing global food security efforts. Elsevier 2019-02-01 Article PeerReviewed Atkinson, Jonathan A., Pound, Michael P., Bennett, Malcolm J. and Wells, Darren M. (2019) Uncovering the hidden half of plants using new advances in root phenotyping. Current Opinion in Biotechnology, 55 . pp. 1-8. ISSN 1879-0429 https://www.sciencedirect.com/science/article/pii/S0958166918300521 doi:10.1016/j.copbio.2018.06.002 doi:10.1016/j.copbio.2018.06.002
spellingShingle Atkinson, Jonathan A.
Pound, Michael P.
Bennett, Malcolm J.
Wells, Darren M.
Uncovering the hidden half of plants using new advances in root phenotyping
title Uncovering the hidden half of plants using new advances in root phenotyping
title_full Uncovering the hidden half of plants using new advances in root phenotyping
title_fullStr Uncovering the hidden half of plants using new advances in root phenotyping
title_full_unstemmed Uncovering the hidden half of plants using new advances in root phenotyping
title_short Uncovering the hidden half of plants using new advances in root phenotyping
title_sort uncovering the hidden half of plants using new advances in root phenotyping
url https://eprints.nottingham.ac.uk/52603/
https://eprints.nottingham.ac.uk/52603/
https://eprints.nottingham.ac.uk/52603/