Disease Detection Techniques in Plants: Transition from Manual to Automation-Reference-Cited by-同舟云学术

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Disease Detection Techniques in Plants: Transition from Manual to Automation

Published:2022-12-03 Issue: Volume: Page:93-109
ISSN:2190-3018
Container-title:New Approaches for Multidimensional Signal Processing
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Author:

Yadav Preeti,Singh Parvinder

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-7842-5_9

Reference41 articles.

1. Sandhu, G.K., Kaur, R.: Plant disease detection techniques: a review. In: International Conference on Automation, Computational and Technology Management, ICACTM 2019, pp. 34–38 (2019). https://doi.org/10.1109/ICACTM.2019.8776827

2. NCRB: NCRB data shows 42,480 farmers and daily wagers committed suicide in 2019—the economic times. The Economic Times (2019)

3. Amutha, D.D.: Present status of Indian agriculture. SSRN Electron. J. (2016). https://doi.org/10.2139/ssrn.2739231

4. Liang, Q., Xiang, S., Hu, Y., Coppola, G., Zhang, D., Sun, W.: PD 2 SE-Net: computer-assisted plant disease diagnosis and severity estimation network. Comput. Electron. Agric. 157, 518–529 (2019). https://doi.org/10.1016/j.compag.2019.01.034

5. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318, (2018). https://doi.org/10.1016/j.compag.2018.01.009

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