Enhancing disease region segmentation in rice leaves using modified deep learning architectures-Reference-Cited by-同舟云学术

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Enhancing disease region segmentation in rice leaves using modified deep learning architectures

Published:2023-12-14 Issue:20 Volume:56 Page:1555-1580
ISSN:0323-5408
Container-title:Archives of Phytopathology and Plant Protection
language:en
Short-container-title:Archives of Phytopathology and Plant Protection

Author:

Sharma Mayuri¹,Kumar Chandan Jyoti²,Singh Thipendra Pal³,Talukdar Jyotismita⁴,Sharma Rupam Kr⁵,Ganguly Ankur⁶

Affiliation:

1. National Institute of Electronics and Information Technology, Guwahati, Assam, India

2. Department of Computer Science and IT, Cotton University, Guwahati, Assam, India

3. School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India

4. Department of CSE, Tezpur University, Tezpur, India

5. Department of CSE, Rajiv Gandhi University, Papum Pare, Arunachal Pradesh, India

6. Royal School of Engineering and Technology, Assam Royal Global University, Guwahati, Assam, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/03235408.2024.2310326

Reference44 articles.

1. Plant growth promotion and suppression of bacterial leaf blight in rice by Paenibacillus polymyxa Sx3

2. Comparison of CNN-based deep learning architectures for rice diseases classification

3. Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

4. An insight into diagnosis of depression using machine learning techniques: a systematic review

5. Semantic segmentation of lungs using a modified U-Net architecture through limited Computed Tomography images

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