Table 6 Comparison Table.
Reference | Classifier | Features | Accuracy |
|---|---|---|---|
ANN, SVM, KNN, NB, and CNN | shape | 91.5% & 90% | |
ANN, KNN, RF, and SVM | GLCM | 94% | |
KNN and Max Voting | Temperature data | 91% | |
DAE, DNN, and ANN | Deep-CNN | 91% | |
CNNC and IKNN | Gradient and extended gradient features | 98.07%, 96.60% | |
Moth Optimization based Deep Neural Network (MO-DNN) | MSO | 0.973% | |
SVM, CNN, and CNN&ViT | CNN and CNN&ViT automated, SVM handcrafted or pre-trained | 91.7%,95.97% & 98.0% | |
k-NN, SVM, ANN, and RF | Color (Lab* components) and texture (LBP) | Disease type 97.4%, Degree of infection 91.0% | |
Proposed model | SVM-Linear | GLCM, LBP, Shape | 99.0% |
