Performance enhancement of kernelized SVM with deep learning features for tea leaf disease prediction
基于深度学习特征的核化SVM在茶叶病害预测中的性能提升
- 关键词：
- 来源：
- Multimedia Tools and Applications 期刊
- 类型：
- 学术文献
- 语种：
- 英语
- 原文发布日期：
- 2023-10-07
- 摘要：
- Due to very limited number of tea leaf images, classification is very difficult. Very frequently overfitting of model occurs. To cope up with this, we applied images augmentation process, that increased dataset nearly fourteen times. But still this number of datasets is not adequate for DL based classification. So, we used here deep learning for feature extraction and machine learning based classifier for classification. In this work, we have proposed a hybrid technique that combines deep learning-based features of augmented dataset with machine learning based classifier for getting better classification result. In proposed work, VGG-16 is used for colour feature extraction from the tea leaf dataset. Based on this feature, model is built and several machine learning-based classifiers like KNN, XGB, Random Forest, and kernelized SVM are employed for classification task. Our proposed model achieved highest classification accuracy with Sigmoid and Linear kernel based SVM and VGG-16 features. The accuracy of proposed model is 96.67%. We compared our proposed work with existing work on tea leaf dataset and found that our model is performing comparatively better.
- 所属专题：
- 60
