A novel framework for image-based plant disease detection using hybrid deep learning approach-Reference-Cited by-同舟云学术

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A novel framework for image-based plant disease detection using hybrid deep learning approach

Published:2022-06-04 Issue: Volume: Page:
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Chug Anuradha,Bhatia Anshul^ORCID,Singh Amit Prakash,Singh Dinesh

Funder

Department of Science and Technology, Ministry of Science and Technology

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-022-07177-7.pdf

Reference64 articles.

1. Abbas A, Jain S, Gour M, Vankudothu S (2021) Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput Electron Agric 187:106279

2. Agrawal T (2021) Optuna and autoML. Hyperparameter optimization in machine learning. Springer, Berkeley CA, pp 109–129

3. Ahmed K, Shahidi TR, Alam SMI, Momen S (2019) Rice leaf disease detection using machine learning techniques. In: 2019 International conference on sustainable technologies for industry 4.0 (STI), pp 1–5

4. Akiba T, Sano S, Yanase T, et al (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2623–2631

5. Alfarisy AA, Chen Q, Guo M (2018) Deep learning based classification for paddy pests & diseases recognition. In: Proceedings of 2018 international conference on mathematics and artificial intelligence, pp 21–25

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