Deep machine learning provides state-of-the art performance in image-based plant phenotyping

Pound, Michael P. and Burgess, Alexandra J. and Wilson, Michael H. and Atkinson, Jonathan A. and Griffiths, Marcus and Jackson, Aaron S. and Bulat, Adrian and Tzimiropoulos, Yorgos and Wells, Darren M. and Murchie, Erik H. and Pridmore, Tony P. and French, Andrew P. (2016) Deep machine learning provides state-of-the art performance in image-based plant phenotyping. Cold Spring Harbor Laboratory.

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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.

Item Type: Other
RIS ID: https://nottingham-repository.worktribe.com/output/790364
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1101/053033
Depositing User: Eprints, Support
Date Deposited: 30 Mar 2017 10:38
Last Modified: 04 May 2020 17:52
URI: http://eprints.nottingham.ac.uk/id/eprint/41648

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