# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 4 Model Evaluation and Discussion
### Discussion
#### 4.4.2 Comparison with Xception Implementation Result
\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** & \begin{tabular}{c} **Time Elapsed** \\ **(hh:mm:ss)** \\ \end{tabular} & \begin{tabular}{c} **Mini-Batch** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **Validation** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **Mini-Batch** \\ **Loss** \\ \end{tabular} &
\begin{tabular}{c} **Validation** \\ **Loss** \\ \end{tabular} \\ \hline
1 & 1 & 00:00:22 & 34.00\% & 58.89\% & 1.7006 & 0.9504 \\ \hline
4 & 30 & 00:02:19 & 80.00\% & 85.56\% & 0.5491 & 0.4368 \\ \hline
7 & 50 & 00:04:00 & 88.00\% & 88.89\% & 0.367 & 0.3421 \\ \hline
8 & 60 & 00:04:18 & 88.00\% & 86.67\% & 0.2642 & 0.3088 \\ \hline
12 & 90 & 00:06:18 & 88.00\% & 91.11\% & 0.388 & 0.5609 \\ \hline
13 & 100 & 00:07:06 & 90.00\% & 88.89\% & 0.2456 & 0.3295 \\ \hline
15 & 120 & 00:08:15 & 92.00\% & 93.33\% & 0.4419 & 0.3259 \\ \hline
19 & 150 & 00:10:14 & 94.00\% & 88.89\% & 0.0522 & 0.3477 \\ \hline
23 & 180 & 00:12:13 & 92.00\% & 90.00\% & 0.1239 & 0.3477 \\ \hline
25 & 200 & 00:13:41 & 94.00\% & 90.00\% & 0.0715 & 0.2749 \\ \hline
27 & 210 & 00:14:12 & 94.00\% & 91.11\% & 0.1271 & 0.2749 \\ \hline
30 & 240 & 00:16:48 & 90.00\% & 91.11\% & 0.2741 & 0.2749 \\ \hline
32 & 250 & 00:17:18 & 90.00\% & 91.11\% & 0.2714 & 0.2749 \\ \hline
35 & 270 & 00:19:10 & 98.00\% & 92.22\% & 0.118 & 0.2396 \\ \hline
38 & 300 & 00:20:11 & 98.00\% & 92.22\% & 0.185 & 0.2316 \\ \hline
40 & 320 & 00:21:34 & 94.00\% & 92.22\% & 0.185 & 0.2316 \\ \hline \multicolumn{7}{p{42.7 pt}}{Test accuracy 0.9333} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Xception model testing accuracy using augmented images. As shown in Table 1, the image preprocessing and segmentation methods that were used in our experiment improved the performance of both classifiers. Also, the image augmentation process that was used to increase the number of images in the dataset improved the classification performance. In addition, the precision and recall results illustrate that the proposed model performs better in recalling the positive samples and also in detecting samples, as most of them were positive samples. This result illustrates that the proposed model achieved better accuracy than the two previous models as shown in Table 6. This is because, as we discussed in Section 3, the layers and parameters of the proposed model were selected based on the problem domain that we wanted to study. In addition, the proposed model has 1.1 million parameters, which is much smaller than the Xception model, which has 23 million parameters, and the InceptionV3 model, which has 24 million parameters. The smaller size of the proposed model makes it more efficient, especially in terms of computational resource usage such as memory. The misclassification in the proposed method is due to the presence of noise or artifacts in the images, which can confuse the model, leading to incorrect predictions. The improper tuning of hyperparameters during training can hinder the model's ability to generalize effectively, and changes in lighting, background, or other environmental factors during image capture can impact the model's performance on real-world data.