Enhancing Rice Crop Health Assessment: Evaluating Disease Identification with a CNN-RF Hybrid Approach-Reference-Cited by-同舟云学术

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Enhancing Rice Crop Health Assessment: Evaluating Disease Identification with a CNN-RF Hybrid Approach

Published:2024-06-07 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET)
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Author:

Govindharaj I¹,Rajput Kapil²,Garg Navin³,Kukreja Vinay⁴,Sharma Rishabh⁴

Affiliation:

1. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Department of Computer Science and Engineering,Chennai,Tamil Nadu,600062

2. Graphic Era Hill University,Computer Science and Engineering,Dehradun,Uttarakhand,India,248002

3. Graphic Era Deemed to be University,Computer Science & Engineering,Dehradun,Uttarakhand,India

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

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10616170/10616264/10616297.pdf?arnumber=10616297

Reference15 articles.

1. DeepLeaf: Revolutionizing Rice Disease Detection and Classification using Convolutional Neural Networks and Random Forest Hybrid Model

2. Plant Disease Identification Using a Novel Convolutional Neural Network

3. Integration of Support Vector Machine and Deep Convolutional Neural Network for Rice Crop Diseases Identification

4. Rice Disease Recognition and Feature Visualization Using a Convolutional Neural Network

5. Disease Detection in Rice leaves using Convolutional Neural Network

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