Tomato Plant Disease Detection and Classification Using Convolutional Neural Network Architectures Technologies-Reference-Cited by-同舟云学术

Tomato Plant Disease Detection and Classification Using Convolutional Neural Network Architectures Technologies

Published:2021-10-02 Issue: Volume: Page:33-44
ISSN:2190-3018
Container-title:Networking, Intelligent Systems and Security
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Author:

Hammou Djalal Rafik,Boubaker Mechab

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-3637-0_3

Reference20 articles.

1. Hughes, D., Salathé, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. arXiv preprint arXiv:1511.08060 (2015): n. pag

2. Huang, G., Liu, Z., K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2017, pp. 4700–4708

3. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July 2016, pp. 770–778 (2016)

4. Bachman, S.: State of the World’s Plants Report. Royal Botanic Gardens, Kew, p. 7/84 (2016) (ISBN 978-1-84246-628-5)

5. Hanssen, I.M., Lapidot, M.: Major tomato viruses in the Mediterranean basin. In: Loebenstein, G., Lecoq, H. (eds.) Advances in Virus Research, vol. 84, pp. 31–66. Academic Press, San Diego (2012)

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