Enhancing Accuracy of Yellowing Disease Severity Level Detection in Coconut Palms with SVM Regularization and CNN Feature Extraction-Reference-Cited by-同舟云学术

Enhancing Accuracy of Yellowing Disease Severity Level Detection in Coconut Palms with SVM Regularization and CNN Feature Extraction

Published:2023-05-26 Issue: Volume: Page:
ISSN:
Container-title:2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)
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Author:

Banerjee Deepak¹,Kukreja Vinay¹,Vats Satvik²,Jain Vishal³,Goyal Bhawna⁴

Affiliation:

1. Chitkara University Institute of Engineering and Technology, Chitkara University,Punjab,India

2. Computer Science and Engineering, Graphic Era Hill University,Dehradun

3. Sharda School of Engineering and Technology, Sharda University,Department of CSE,Greater Noida,UP,India

4. Chandigarh University,Department of ECE and UCRD,Mohali,Punjab,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10176330/10176362/10176557.pdf?arnumber=10176557

Reference17 articles.

1. Coconut malformation: An emerging disease caused by Fusarium proliferatum in southern Iran

2. Molecular and biological characterization of phytoplasmas from coconut palms affected by the lethal yellowing disease in Africa

3. Disease and pest infection detection in coconut tree through deep learning techniques;piyush;Comput Electron Agric,2021

4. Coconut lethal yellowing disease and the oryctes monoceros beetle: A joint venture against coconut production in Mozambique;joao;Phytopathogenic Mollicutes 9,2019

5. Coconut disease prediction system using image processing and deep learning techniques;dhapitha;2020 IEEE 4th International Conference on Image Processing Applications and Systems (IPAS),0

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