Deep machine learning provides state-of-the-art performance in image-based plant phenotyping-Reference-Cited by-同舟云学术

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

Published:2017-08-23 Issue:10 Volume:6 Page:
ISSN:2047-217X
Container-title:GigaScience
language:en
Short-container-title:

Author:

Pound Michael P.¹,Atkinson Jonathan A.²,Townsend Alexandra J.²,Wilson Michael H.³,Griffiths Marcus²,Jackson Aaron S.¹,Bulat Adrian¹,Tzimiropoulos Georgios¹,Wells Darren M.²,Murchie Erik H.²,Pridmore Tony P.¹,French Andrew P.¹²

Affiliation:

1. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK

2. School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nr Loughborough, LE12 5RD, UK

3. Centre for Plant Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

Link

http://academic.oup.com/gigascience/article-pdf/6/10/gix083/25512994/gix083.pdf

Reference31 articles.

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2. Computer vision cracks the leaf code;Wilf;Proc Natl Acad Sci U S A,2016

3. Random decision forests;Ho.,1995

4. Machine learning for high-throughput stress phenotyping in plants;Singh;Trends Plant Sci,2016

5. Gradient-based learning applied to document recognition;Lecun;Proc IEEE,1998

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