## 4 Results
### Performance of All Five DL Models on IDCBS10 with and without (K5-CV) and MajMulVot  
In this experimental protocol, we use the bamboo plant image dataset IDCBS10 to evaluate the training time, accuracy, and performance of all five CNN models with respect to layers. First, we compare the average training times required for these five DL models on the bamboo image datasets and their respective layer counts. The VGG19 network, consisting of 19 layers, has the longest average training time, which amounts to 14 min and 19 s. In contrast, the GoogleNet network, with 22 layers, has a minimum average training time of 8 min and 31 s. The impact of deep layers on the training duration of five convolutional neural networks (CNNs) is shown in Figure 11 using a line chart. The figure demonstrates varying training times as the number of layers increases. However, the duration of training for CNN models is contingent upon the quantity of parameters inside the pre-trained network. Second, we evaluate the performance of all five CNN classifiers in terms of classification accuracy, F1-score, sensitivity, specificity, PPV, and NPV. In this protocol, first, we apply all five CNN models (AlexNet, VGG16, ResNet18, VGG19, GoogleNet) on dataset IDCBS10 without K5-CV and next with K5-CV and MajMulVot. The comparative result of both approaches is described in Table 9. Both approaches perform well with and without VGG16 compared to other DL models. In the first approach, i.e., without using any cross-validation, the VGG16 model performs well among all DL models, with the result as follows: ACC: 88.75, F1S: 91.66, SEN: 92.8, SPC: 93.05, PPV: 91.25, and NPV: 86.84. When we apply K5-CV, the accuracy of all DL models is enhanced. We can see that the DL-MajMulVot model performs well on the IDCBS10 bamboo dataset, giving the highest accuracy of 92.8%, as well as scaling up all performance metrics (ACC: 92.8, F1S: 93.75, SEN: 100, SPC: 90.47, PPV: 923.23, NPV: 100). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **ML Classifier** & \multicolumn{5}{c}{**Performance Metric (\%)**} \\ \hline
**DS2** & & **ACC** & **F1S** & **SEN** & **SPC** & **PPV** & **NPV** \\ \hline \multirow{6}{*}{Without K5-CV} & Logistic Regression & 77.85 & 72.30 & 69.19 & 91.65 & 76.02 & 86.93 \\  & Decision Tree & 81.89 & 77.83 & 76.11 & 89.11 & 79.69 & 86.11 \\  & Random Forest & 81.96 & 80.93 & 79.05 & 90.08 & 82.54 & 86.74 \\  & K-Nearest Neighbors & 77.72 & 64.09 & 56.32 & 94.61 & 80.77 & 86.23 \\  & Support Vector Machine & 82.27 & 88.79 & 90.55 & 97.67 & 81.04 & 71.74 \\ \hline \multirow{6}{*}{With K5-CV} & Logistic Regression & 78.20 & 78.68 & 76.06 & 92.77 & 81.63 & 86.44 \\  & Decision Tree & 82.33 & 83.40 & 77.69 & 90.92 & 87.80 & 87.69 \\ \cline{1-1}  & Random Forest & 82.07 & 84.12 & 75.23 & 93.36 & 90.08 & 88.15 \\ \cline{1-1}  & K-Nearest Neighbors & 78.08 & 69.51 & 79.20 & 89.71 & 83.21 & 85.93 \\ \cline{1-1}  & Support Vector Machine & 84.11 & 90.19 & 89.45 & 96.91 & 90.94 & 88.58 \\ \cline{1-1}  & ML-MajMulVot & 86.96 & 85.93 & 84.05 & 95.08 & 87.54 & 91.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of all five ML models on DS2 with/without K5-CV and MajMulVot. Furthermore, this experimental methodology examines the influence of the CNN layers on the accuracy. Figure 12 illustrates the correlation between the mean accuracy of each convolutional neural network (CNN) model and the number of layers. Among the CNN models, VGG16, a 16-layer deep model, exhibits the best average accuracy of 89.50%, while the lowest average accuracy of 83.30% is recorded using a 22-layer deep network known as GoogleNet. The proposed DL-MajMulVot approach gives the highest accuracy of 92.8%. The relationship between the accuracy and layer count for the five convolutional neural networks (CNNs) is shown in Figure 12 using a line chart. This demonstrates an incongruuous relationship between the precision and the increase in layers.