# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 4 Model Evaluation and Discussion
### Performance of Proposed Model Trained with Original Images  
As shown in the graph in Figure 6, both validation accuracy and training accuracy increase when we move from the first epoch to the last epoch. On the other hand, both validation loss and training loss decrease. In this experiment, the validation accuracy and training accuracy in the final batch are 92.22% and 94%, respectively. This indicates that the model can effectively learn from the training data and generalize well to the validation data, suggesting good convergence of the training process. Also, the validation loss and training loss in the final batch are 0.2319 and 0.1485, respectively. The consistent decrease in both training and validation loss further confirms the convergence of the optimization process. The close alignment between the training and validation metrics, with the validation performance closely tracking the training performance, suggests that the model is not overfitting the training data. In Figure 6, the black line at the top corresponds to the training accuracy, while the blue line indicates the validation accuracy. Below that, the red line represents the validation loss, and the black line shows the training loss.  
As shown in Table 1, the model was 90% accurate when tested with the test set data. This classification accuracy was achieved when the model was trained without preprocessing, segmentation, or data augmentation. The simulation took 24 min to train (36 s per each epoch (40)). In experiments 1 and 2, 360 testing images were employed and divided into three classes: phyllody disease class, bacterial blight disease class, and healthy class. In experiment 1, 36 images out of the 360 sesame leaf images were misclassified.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Epoch** & **Iteration** & **Time Elapsed** & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
1 & 1 & 0:00:43 & 34.00\% & 50.00\% & 2.2525 & 1.1228 \\ \hline
4 & 30 & 0:03:12 & 76.00\% & 84.44\% & 0.4858 & 0.4692 \\ \hline
7 & 50 & 0:04:46 & 74.00\% & 92.22\% & 0.5023 & 0.2504 \\ \hline
8 & 60 & 0:05:35 & 92.00\% & 92.22\% & 0.1682 & 0.2504 \\ \hline
12 & 90 & 0:08:02 & 88.00\% & 91.11\% & 0.3048 & 0.2727 \\ \hline
13 & 100 & 0:08:49 & 92.00\% & 91.11\% & 0.1922 & 0.2776 \\ \hline
15 & 120 & 0:10:20 & 96.00\% & 94.44\% & 0.0737 & 0.1889 \\ \hline
19 & 150 & 0:12:36 & 92.00\% & 94.44\% & 0.2408 & 0.1923 \\ \hline
23 & 180 & 0:14:38 & 96.00\% & 94.44\% & 0.1399 & 0.1923 \\ \hline
25 & 200 & 0:15:58 & 98.00\% & 94.44\% & 0.042 & 0.1641 \\ \hline
27 & 210 & 0:16:40 & 96.00\% & 93.33\% & 0.0835 & 0.1793 \\ \hline
30 & 240 & 0:18:40 & 94.00\% & 94.44\% & 0.0284 & 0.1692 \\ \hline
32 & 250 & 0:19:19 & 100.00\% & 94.44\% & 0.0299 & 0.1692 \\ \hline
34 & 270 & 0:20:38 & 98.00\% & 91.11\% & 0.1247 & 0.2036 \\ \hline
38 & 300 & 0:22:38 & 94.00\% & 91.11\% & 0.1485 & 0.2319 \\ \hline
40 & 320 & 0:24:01 & 94.00\% & 91.11\% & 0.1485 & 0.2319 \\ \hline \multicolumn{7}{l}{Test accuracy 0.9} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testing accuracy of original images.  
Figure 6: Training and validation accuracy using original images.