A Hybrid CNN and Random Forest Approach for Wheat Disease Detection-Reference-Cited by-同舟云学术

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A Hybrid CNN and Random Forest Approach for Wheat Disease Detection

Published:2023-12-29 Issue: Volume: Page:1-5
ISSN:
Container-title:2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)
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Author:

Kumar Varun¹,Banerjee Deepak¹,Chauhan Rahul²,Thapliyal Siddhant³,Sharma Keshav⁴,Gill Kanwarpartap Singh

Affiliation:

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

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

3. Graphic Era Deemed to be University,Dehradun,Uttarakhand,India,248002

4. Chandigarh group of colleges,Landran,Punjab,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10441789/10441802/10441988.pdf?arnumber=10441988

Reference14 articles.

1. Ransomware Attack Detection in the Internet of Things using Machine Learning Approaches

2. LUFlow: Attack Detection in the Internet of Things Using Machine Learning Approaches;Babbar;2023 Int. Conf. Distrib. Comput. Electr. Circuits Electron.,2023

3. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning

4. Multiple Classifier Combination For Recognition Of Wheat Leaf Diseases

5. Precision Agriculture: Classifying Banana Leaf Diseases with Hybrid Deep Learning Models

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