Deep machine learning provides state-of-the art performance in image-based plant phenotyping
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 identific...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Other |
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Cold Spring Harbor Laboratory
2016
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| Online Access: | https://eprints.nottingham.ac.uk/41648/ |
| _version_ | 1848796323423191040 |
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| author | Pound, Michael P. Burgess, Alexandra J. Wilson, Michael H. Atkinson, Jonathan A. Griffiths, Marcus Jackson, Aaron S. Bulat, Adrian Tzimiropoulos, Yorgos Wells, Darren M. Murchie, Erik H. Pridmore, Tony P. French, Andrew P. |
| author_facet | Pound, Michael P. Burgess, Alexandra J. Wilson, Michael H. Atkinson, Jonathan A. Griffiths, Marcus Jackson, Aaron S. Bulat, Adrian Tzimiropoulos, Yorgos Wells, Darren M. Murchie, Erik H. Pridmore, Tony P. French, Andrew P. |
| author_sort | Pound, Michael P. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:46:09Z |
| format | Other |
| id | nottingham-41648 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:46:09Z |
| publishDate | 2016 |
| publisher | Cold Spring Harbor Laboratory |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-416482020-05-04T17:52:17Z https://eprints.nottingham.ac.uk/41648/ Deep machine learning provides state-of-the art performance in image-based plant phenotyping Pound, Michael P. Burgess, Alexandra J. Wilson, Michael H. Atkinson, Jonathan A. Griffiths, Marcus Jackson, Aaron S. Bulat, Adrian Tzimiropoulos, Yorgos Wells, Darren M. Murchie, Erik H. Pridmore, Tony P. French, Andrew P. 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. Cold Spring Harbor Laboratory 2016-05-12 Other NonPeerReviewed Pound, Michael P., Burgess, Alexandra J., Wilson, Michael H., Atkinson, Jonathan A., Griffiths, Marcus, Jackson, Aaron S., Bulat, Adrian, Tzimiropoulos, Yorgos, Wells, Darren M., Murchie, Erik H., 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. http://biorxiv.org/content/early/2016/05/12/053033 doi:10.1101/053033 doi:10.1101/053033 |
| spellingShingle | Pound, Michael P. Burgess, Alexandra J. Wilson, Michael H. Atkinson, Jonathan A. Griffiths, Marcus Jackson, Aaron S. Bulat, Adrian Tzimiropoulos, Yorgos Wells, Darren M. Murchie, Erik H. Pridmore, Tony P. French, Andrew P. Deep machine learning provides state-of-the art performance in image-based plant phenotyping |
| title | Deep machine learning provides state-of-the art performance in image-based plant phenotyping |
| title_full | Deep machine learning provides state-of-the art performance in image-based plant phenotyping |
| title_fullStr | Deep machine learning provides state-of-the art performance in image-based plant phenotyping |
| title_full_unstemmed | Deep machine learning provides state-of-the art performance in image-based plant phenotyping |
| title_short | Deep machine learning provides state-of-the art performance in image-based plant phenotyping |
| title_sort | deep machine learning provides state-of-the art performance in image-based plant phenotyping |
| url | https://eprints.nottingham.ac.uk/41648/ https://eprints.nottingham.ac.uk/41648/ https://eprints.nottingham.ac.uk/41648/ |