The use of plant models in deep learning: an application to leaf counting in rosette plants-Reference-Cited by-同舟云学术

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The use of plant models in deep learning: an application to leaf counting in rosette plants

Published:2018-01-18 Issue:1 Volume:14 Page:
ISSN:1746-4811
Container-title:Plant Methods
language:en
Short-container-title:Plant Methods

Author:

Ubbens Jordan,Cieslak Mikolaj,Prusinkiewicz Przemyslaw,Stavness Ian

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Plant Science,Genetics,Biotechnology

Link

http://link.springer.com/content/pdf/10.1186/s13007-018-0273-z.pdf

Reference37 articles.

1. Furbank RT, Tester M. Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011;16(12):635–44.

2. Lemnatec. http://www.lemnatec.com . Accessed 01 Aug 2017.

3. Minervini M, Giuffrida MV, Perata P, Tsaftaris SA. Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants. Plant J. 2017;90(1):204–16. https://doi.org/10.1111/tpj.13472 .

4. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. ImageNet: a large-scale hierarchical image database. In: The IEEE conference on computer vision and pattern recognition (CVPR), Miami Beach, FL, USA; 2009.

5. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–4. https://doi.org/10.1038/nature14539 , arXiv:1312.6184v5 .

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