Classification of Camellia oleifera using a dual recognition strategy based on deep learning-Reference-Cited by-同舟云学术

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Classification of Camellia oleifera using a dual recognition strategy based on deep learning

Published:2024-05-30 Issue: Volume: Page:
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Meng Zhichao,Du Xiaoqiang,Yao Xiaohua,He Leiying,Lin Lepeng

Funder

the National Key Research and Development Program of China

the Zhejiang Provincial Natural Science Foundation

the Zhejiang Provincial Key Research & Development Plan

Project of Hangzhou Science and Technology Bureau

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-19413-3.pdf

Reference38 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. https://doi.org/10.1016/j.compag.2021.106279

2. Arel I, Rose DC, Karnowski TP (2010) Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Computer Intell M 5(4):13–18. https://doi.org/10.1109/MCI.2010.938364

3. Arshaghi A, Ashourian M, Ghabeli L (2023) Potato diseases detection and classification using deep learning methods. Multimed Tools Appl 82:5725–5742

4. Barbedo JGA (2016) A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52–60. https://doi.org/10.1016/j.biosystemseng.2016.01.017

5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

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