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Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

View ORCID ProfileMichael P. Pound, View ORCID ProfileAlexandra J. Burgess, View ORCID ProfileMichael H. Wilson, View ORCID ProfileJonathan A. Atkinson, Marcus Griffiths, Aaron S. Jackson, Adrian Bulat, View ORCID Profileyorgos Tzimiropoulos, View ORCID ProfileDarren M. Wells, View ORCID ProfileErik H. Murchie, View ORCID ProfileTony P. Pridmore, View ORCID ProfileAndrew P. French
doi: https://doi.org/10.1101/053033
Michael P. Pound
1The School of Computer Science, University of Nottingham,UK
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Alexandra J. Burgess
2The School of Biosciences, University of Nottingham, UK
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Michael H. Wilson
3Centre for Plant Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, UK
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Jonathan A. Atkinson
2The School of Biosciences, University of Nottingham, UK
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Marcus Griffiths
2The School of Biosciences, University of Nottingham, UK
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Aaron S. Jackson
1The School of Computer Science, University of Nottingham,UK
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Adrian Bulat
1The School of Computer Science, University of Nottingham,UK
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yorgos Tzimiropoulos
1The School of Computer Science, University of Nottingham,UK
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Darren M. Wells
2The School of Biosciences, University of Nottingham, UK
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Erik H. Murchie
2The School of Biosciences, University of Nottingham, UK
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Tony P. Pridmore
1The School of Computer Science, University of Nottingham,UK
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Andrew P. French
1The School of Computer Science, University of Nottingham,UK
2The School of Biosciences, University of Nottingham, UK
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  • For correspondence: andrew.p.french@nottingham.ac.uk
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Abstract

Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

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Posted May 12, 2016.
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Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping
Michael P. Pound, Alexandra J. Burgess, Michael H. Wilson, Jonathan A. Atkinson, Marcus Griffiths, Aaron S. Jackson, Adrian Bulat, yorgos Tzimiropoulos, Darren M. Wells, Erik H. Murchie, Tony P. Pridmore, Andrew P. French
bioRxiv 053033; doi: https://doi.org/10.1101/053033
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Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping
Michael P. Pound, Alexandra J. Burgess, Michael H. Wilson, Jonathan A. Atkinson, Marcus Griffiths, Aaron S. Jackson, Adrian Bulat, yorgos Tzimiropoulos, Darren M. Wells, Erik H. Murchie, Tony P. Pridmore, Andrew P. French
bioRxiv 053033; doi: https://doi.org/10.1101/053033

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