EnC-SVMWEL: Ensemble Approach using CNN and SVM Weighted Average Ensemble Learning for Sugarcane Leaf Disease Detection

Author:

Vignesh Uvarani1,Chokkalingam Bala Subramanian2

Affiliation:

1. Kalasalingam Research Academy of Research and Education, Krishnankoil,Department of IT,VirudhunagarDistrict,Tamilnadu,India

2. Kalasalingam Academy of Research and Education, Krishnankoil,Department of CSE,VirudhunagarDistrict,Tamilnadu,India

Publisher

IEEE

Reference24 articles.

1. VGG-ICNN: A Lightweight CNN model for crop disease identification

2. Efficient Hyperparameter Optimization of XGBoost and SVM Using Bayesian Optimization;yang;Proceedings of the AAAI Conference on Artificial Intelligence,2020

3. Cardamom plant disease detection approach using EfficientNetV2;sunil;IEEE Access,2021

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1. Sugarcane Severity Assessment with Deep Learning and Random Forest: A Multi-class Approach;2024 11th International Conference on Signal Processing and Integrated Networks (SPIN);2024-03-21

2. Enhancing Sugarcane Crop Health: CNN and SVM-Based Predictive Analysis of Leaf Diseases;2024 3rd International Conference for Innovation in Technology (INOCON);2024-03-01

3. Sugarcane Leaf Health Grading Using State-of-the-Art Deep Learning Approaches;2024 3rd International Conference for Innovation in Technology (INOCON);2024-03-01

4. Sugarcane Severity Assessment with Deep Learning and Random Forest: A Multi-Class Approach;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29

5. A Robust Index Based on Phenological Features to Extract Sugarcane from Multisource Remote Sensing Data;Remote Sensing;2023-12-18

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