Plant Detection and Classification Using Fast Region-Based Convolution Neural Networks-Reference-Cited by-同舟云学术

Plant Detection and Classification Using Fast Region-Based Convolution Neural Networks

Published:2020 Issue: Volume: Page:623-634
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Lochan Raja Naga,Tomar Anoop Singh,Srinivasan R.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-0199-9_54

Reference18 articles.

1. Heywood, V.H., Watson: Global Biodiversity Assessment. Cambridge University Press, Cambridge, UK (1995)

2. Loreau, M., et al.: Diversity without representation. Nature 442, 245–246 (2006)

3. Pimm, S.L., Russell, G.J., Gittleman, J.L., Brooks, T.M.: The future of biodiversity. Science 269, 347–350 (1995)

4. Wilson, E.O.: The current state of biological diversity. In: Wilson, E.O. (ed.) Biodiversity, pp. 3–18. National Academy Press, Washington, DC (1988)

5. Cope, J.S., Corney, D., Clark, J.Y., Remagnino, P., Wilkin, P.: Plant species identification using digital morphometrics: a review. Expert Syst. Appl. 39(8), 7562–7573 (2012)

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