Performance Analysis of Hand-Crafted Features and CNN Toward Real-Time Crop Disease Identification-Reference-Cited by-同舟云学术

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Performance Analysis of Hand-Crafted Features and CNN Toward Real-Time Crop Disease Identification

Published:2020-10-22 Issue: Volume: Page:497-505
ISSN:2190-3018
Container-title:Information and Communication Technology for Intelligent Systems
language:
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Author:

Tiwari Vivek,Agrahari Aditi,Srivastava Sriyuta

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-7078-0_48

Reference26 articles.

1. Liakos, K.G., Busato, P., Moshou, D., Pearson, S., Bochtis, D.: Machine learning in agriculture: a review. Sensors 18(8), 2674 (2018)

2. Ramesh, M.S., Vinod, P.V., Niveditha, M., Pooja, R., Shashank, N., and Hebbar, R.: Plant disease detection using machine learning. In: 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), pp. 41–45, IEEE (2018)

3. Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., Stefanovic, D.: Solving current limitations of deep learning based approaches for plant disease detection. Symmetry 11(7), 939 (2019)

4. Caglayan, A., Guclu, O., Can, A.B.: A plant recognition approach using shape and color features in leaf images. In: Petrosino, A. (eds.) Image Analysis and Processing—ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol. 8157. Springer, Berlin, Heidelberg (2013)

5. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

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