# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 4 Results  
The performance of the proposed FF-PCA-LDA approach is evaluated on the benchmark dataset Section 3.1. The experimental setup involves a Core i5 PC equipped with 16 GB RAM, along with an NVIDIA GeForce GTX 1050 TI GPU and Matlab 2020(a). Experiments are performed to determine the capability of feature fusion along with PCA-based feature reduction followed by LDA classification using ordinary RGB potato leaf images. Figure 5a,b show the graphical representation of PC1 and PC2 of the handcrafted and deep features, respectively. The feature fusion visualization with respect to its first and second principal components, PC1 and PC2, is shown in Figure 5c. The performance evaluations in terms of sensitivity, specificity, and F-Score are shown in Figure 6. Class-wise ROC curves of the developed approach are depicted in Figure 7.  
Table 1 shows the classification performance of the LDA on 437 handcrafted features, achieving an accuracy of 92.21%. The feature dimension is reduced by employing PCA, and the top 100 PCs (principal components) are utilized to build the LDA model. The \(PCA-LDA_{HH}\) classification model offers an average accuracy of 96.53%. Although the average accuracy of handcrafted-features-based PCA and LDA classifications is sufficiently high, among classes, we can observe that the _Healthy_ class's performance is relatively low.  
Table 2 shows the LDA classification performance on deep features extracted by the modified ResNet50, which offered an average accuracy of 97.96%. Moreover, the PCA-based FF and LDA offer an average accuracy of 98.20%.  
The comparison of the proposed approach with other classifiers is shown in Table 3. The developed approach performance is compared with published approaches in Table 4. The average AUC of 98.46% is obtained from the ROC curves (Figure 7).  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Class/Model**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\  & \(LDA_{HH}\) & \(PCA-LDA_{HH}\) \\ \hline Healthy & 89.26 & 96.20 \\ Early Blight & 93.43 & 98.20 \\ Late Blight & 90.96 & 95.20 \\
**Average** & **92.21** & **96.53** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy of handcrafted hybrid (HH)-features-based models for potato leaf disease identification.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Class/Model**} & \multirow{2}{*}{**TL-ResNet50**} & \multirow{2}{*}{\(PCA-LDA_{DF}\)} & \multirow{2}{*}{**Proposed PCA \(-LDA_{FF}\)**} \\  & & & \\ \hline Healthy & 96.76 & 98.53 & 98.20 \\ Early blight & 96.51 & 94.73 & 99.10 \\ Late blight & 96.26 & 97.63 & 97.31 \\
**Average** & **96.51** & **97.96** & **98.20** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance classification accuracy (%) of TL-ResNet50, \(PCA-LDA_{DF}\) and \(PCA-LDA_{FF}\) models using Transfer learning, deep features and fused features, respectively.  
Figure 5: Representation of class discrimination of various feature types by PCs.  
Figure 6: Performance evaluation of the proposed approach on test dataset.  
Figure 7: Class-wise ROC curves of the proposed FF-PCA-LDA approach.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Classifiers** & **Accuracy (\%)** \\ \hline MLBPNN (Hidden units = 50) & \\ Multi-layer back-propagation neural networks & 72.00 \\ KNN (with k = 3) K-nearest neighbor & 76.90 \\ PNN (probabilistic neural networks) & 71.25 \\
**The proposed FF-PCA-LDA approach** & **98.20** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the proposed approach with other classifiers.