# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 4 Model Evaluation and Discussion
### Performance of Proposed Model Trained with Augmented Images  
In experiment 3, as Figure 8 shows, the validation accuracy and training accuracy were 97.78% and 98% respectively. Also, the validation loss and training loss in the final batch were 0.0466 and 0.2090, respectively. The consistent decrease in both training and validation loss, with a final validation loss of 0.05 and training loss of 0.21, further confirms the optimization process. Compared to the previous experiment, the model has improved its validation accuracy by 2.22 percentage points and reduced the validation loss from 0.52 to 0.05, suggesting continued progress. To enable a more comprehensive analysis and comparison with existing methods, additional details on the neural network architecture, hyperparameters, and evaluation of other datasets would be helpful in assessing the strengths and limitations of the proposed approach and its potential for broader application. In Figure 8, the black line at the top corresponds to the validation accuracy, while the blue line indicates the training accuracy. Below that, the red line represents the training loss, and the black line shows the validation loss. Also, Table 3 shows that the model was 96.67% accurate when tested with the test set data. In this experiment, 1440 testing images were employed and divided into three classes: phyllody disease class, bacterial blight disease class, and healthy class. Only 47 images out of the 1440 sesame leaf images were misclassified. The training and testing accuracy each improved by 2% as compared with that of the model trained before augmentation. This is because augmentation allows the model to train more features and memorize exact details of the images. The experimental results indicate that the model performance here was better for the recognition and classification of sesame disease. The training accuracy, validation accuracy, and testing accuracy were almost equal; therefore, there was no overfitting or underfitting problem in this experiment. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Epoch** & **Iteration** & **Time Elapsed** & \begin{tabular}{l} **Mini-Batch** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{l} **Validation** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{l} **Mini-Batch** \\ **Loss** \\ \end{tabular} &
\begin{tabular}{l} **Validation** \\ **Loss** \\ \end{tabular} \\ \hline
12 & 90 & 0:07:27 & 70.00\% & 90.00\% & 0.7554 & 0.4131 \\ \hline
13 & 100 & 0:08:08 & 90.00\% & 91.11\% & 0.19 & 0.4551 \\ \hline
15 & 120 & 0:09:30 & 98.00\% & 91.11\% & 0.0656 & 0.396 \\ \hline
19 & 150 & 0:11:27 & 86.00\% & 92.22\% & 0.2381 & 0.4348 \\ \hline
23 & 180 & 0:13:23 & 88.00\% & 88.89\% & 0.3693 & 0.5112 \\ \hline
25 & 200 & 0:14:38 & 100.00\% & 88.89\% & 0.0447 & 0.5112 \\ \hline
27 & 210 & 0:15:19 & 94.00\% & 91.11\% & 0.2933 & 0.4465 \\ \hline
30 & 240 & 0:17:22 & 92.00\% & 91.11\% & 0.1948 & 0.4905 \\ \hline
32 & 250 & 0:18:00 & 94.00\% & 90.00\% & 0.2031 & 0.4905 \\ \hline
34 & 270 & 0:19:19 & 96.00\% & 91.11\% & 0.2284 & 0.4856 \\ \hline
38 & 300 & 0:21:15 & 96.00\% & 91.11\% & 0.1672 & 0.5162 \\ \hline
40 & 320 & 0:22:34 & 96.00\% & 91.11\% & 0.1672 & 0.5162 \\ \hline Test accuracy 0.9444 & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._Figure 8: Training and validation accuracy using augmented images.