## 4 Results
### Classification Performance Analysis  
The classification performance of the proposed method was evaluated using various CNN-based and handcrafted feature extraction techniques, in combination with different machine learning classifiers. The accuracy results obtained from different feature extraction and classification models are presented in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline SVM & Kernel Type & Linear \\ \hline RF & Number of Trees & 100 \\ \hline \multirow{4}{*}{ANN} & Hidden Layers & (512, 256, 128) \\  & Activation Function & ReLU \\ \cline{1-1}  & Learning Rate & Adaptive (initial: 0.001) \\ \cline{1-1}  & Early Stopping & Enabled (patience: 10 epochs) \\ \cline{1-1}  & Maximum Iterations & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Key hyperparameters of the classification models. Among the single feature descriptors, ResNet50 achieved the highest accuracy, obtaining 91.58% with ANN and 90.69% with SVM. However, when hybrid feature combinations were considered, ResNet50 + HSV + GLCM + Gabor yielded the highest accuracy across all classifiers, reaching 92.55% with ANN, 87.22% with RF, and 91.05% with SVM. These results demonstrate that combining deep learning-based features (ResNet50) with handcrafted features (HSV, GLCM, and Gabor) significantly improves classification performance. While individual CNN features provided strong results, integrating handcrafted descriptors enhanced the ability to distinguish infected leaves. To further assess the model performance, a confusion matrix was generated for the best-performing feature combination (ResNet50 + HSV + GLCM + Gabor), as presented in Table 5.  
From the confusion matrix, the model correctly identified 2119 positive cases (infected maize leaves), but 128 samples were misclassified as negative. Similarly, 1686 negative samples (healthy leaves) were correctly identified, while 192 were falsely classified as positive. This indicates a high recall rate for infected leaves, but slight misclassifications between mild infections and healthy samples. To further evaluate classification performance, Precision, Recall, and F1-score were analyzed for different feature combinations. Figures 7-9 illustrate the respective scores. The highest precision was obtained using the ResNet50 + HSV + GLCM + Gabor combination, reaching 93.61% with the ANN classifier (Figure 7). \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Features** & **ANN** & **RF** & **SVM** \\ \hline Single descriptor & Dense201 & 0.8969 & 0.8538 & 0.8875 \\  & EfficientNetB2 & 0.9008 & 0.8518 & 0.8838 \\  & Resnet50 & 0.9158 & 0.8591 & 0.9069 \\  & Gabor & 0.7619 & 0.7248 & 0.7609 \\  & HSV & 0.8591 & 0.8567 & 0.7936 \\  & GLCM & 0.8416 & 0.8300 & 0.7781 \\ \hline Hybrid of two descriptors & Dense201 + Gabor & 0.9025 & 0.8601 & 0.8870 \\  & Dense201 + HSV & 0.9107 & 0.8649 & 0.8969 \\  & Dense201 + GLCM & 0.9071 & 0.8598 & 0.8884 \\  & EfficientNetB2 + Gabor & 0.8986 & 0.8543 & 0.8841 \\  & EfficientNetB2 + HSV & 0.9064 & 0.8664 & 0.8901 \\  & EfficientNetB2 + GLCM & 0.9015 & 0.8552 & 0.8863 \\  & Resnet50 + Gabor & 0.9136 & 0.8700 & 0.9023 \\  & Resnet50 + HSV & 0.9195 & 0.8649 & 0.9083 \\  & Resnet50 + GLCM & 0.9190 & 0.8608 & 0.9054 \\  & HSV + GLCM + Gabor & 0.8613 & 0.8572 & 0.8016 \\  & HSV + GLCM & 0.8775 & 0.8581 & 0.8361 \\  & HSV + Gabor & 0.8496 & 0.8540 & 0.7920 \\ \hline Hybrid of three descriptors & Resnet50 + HSV + GLCM & 0.9216 & 0.8698 & 0.9076 \\  & EfficientNetB2 + HSV + GLCM & 0.9112 & 0.8666 & 0.8957 \\  & Dense201 + HSV + GLCM & 0.9139 & 0.8627 & 0.8967 \\ \hline Hybrid of four descriptors & Resnet50 + HSV + GLCM + Gabor & 0.9255 & 0.8722 & 0.9105 \\  & EfficientNetB2 + HSV + GLCM + Gabor & 0.9071 & 0.8560 & 0.8923 \\  & Dense201 + HSV + GLCM + Gabor & 0.9098 & 0.8627 & 0.8976 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy Scores for Healthy and Infected Maize Leaves.