Enhancing Green Bean Anthracnose Severity Detection via Integrated CNN-LSTM Models-Reference-Cited by-同舟云学术

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Enhancing Green Bean Anthracnose Severity Detection via Integrated CNN-LSTM Models

Published:2024-06-21 Issue: Volume: Page:1-4
ISSN:
Container-title:2023 4th International Conference on Intelligent Technologies (CONIT)
language:
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Author:

Dhawan Nikhil¹,Sharma Rishabh¹,Chattopadhyay Saumitra²,Choudhary Ankur³,Jain Vishal⁴

Affiliation:

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

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,248002

4. Sharda University,Sharda School of Engineering and Technology,Department of Computer Science and Engineering,Greater Noida,U.P.,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10625770/10625923/10626282.pdf?arnumber=10626282

Reference14 articles.

1. Multi-Stage Classification of Pomegranate Anthracnose Disease Severity Levels with CNN and SVM

2. An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound

3. Deep Learning Based Sugarcane Downy Mildew Disease Detection Using CNN-LSTM Ensemble Model for Severity Level Classification

4. Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests

5. A Smart Approach to Coconut Leaf Spot Disease Classification using Computer Vision and Deep Learning Technique

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