Sugarcane Severity Assessment with Deep Learning and Random Forest: A Multi-Class Approach
Author:
Affiliation:
1. Chitkara University Institute of Engineering and Technology, Chitkara University,Punjab,India
2. Graphic Era Hill University,Dehradun,Uttarakhand,India,248002
3. Graphic Era Deemed to be University,Dehradun,Uttarakhand,India,248002
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10441789/10441802/10442627.pdf?arnumber=10442627
Reference16 articles.
1. Red Rot Disease Prediction in Sugarcane Using the Deep Learning Approach
2. Tea Leaf Diseases Classification and Detection using a Convolutional Neural Network
3. SLViT: Shuffle-convolution-based lightweight Vision transformer for effective diagnosis of sugarcane leaf diseases
4. EnC-SVMWEL: Ensemble Approach using CNN and SVM Weighted Average Ensemble Learning for Sugarcane Leaf Disease Detection
5. Enhancing Mango Fruit Disease Severity Assessment with CNN and SVM-Based Classification
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