# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 4 Model Evaluation and Discussion
### Discussion
#### 4.4.2 Comparison with Xception Implementation Result  
The Xception convolutional neural network can classify images into 1000 class categories. Here, the different spatial dimensions (1 \(\times\) 1, 5 \(\times\) 5, 3 \(\times\) 3) of the Inception architecture are replaced with a single dimension (3 \(\times\) 3) followed by a 1 \(\times\) 1 convolution. In the 3 \(\times\) 3 convolutions, spatial correlations within each channel are captured, and in the 1 \(\times\) 1 convolutions, the cross-feature map is captured. This replacement architecture allows it to regulate its computational complexity. It has 71 layers with 23 million parameters. From those layers, the 36 convolutional layers form the feature extraction base of the network and have an input image size of 299 by 299 [27]. As shown in Figure 10, the validation accuracy and training accuracy are 92.2% and 94%, respectively. The validation loss and training loss in the final batch are 0.2316 and 0.1850, respectively. In Figure 10, 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 5 shows that the test accuracy of the model was 93.3%. When compared with the proposed model, the training and testing accuracy was decreased by 4% and 3%, respectively. This model took around 5 h and 57 min to train, with 535 s for each epoch (40). The accuracy is lower than the proposed model, and takes a lot more time to compute. \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
1 & 1 & 00:00:38 & 38.00\% & 43.33\% & 1.4999 & 1.1412 \\ \hline
4 & 30 & 00:02:46 & 70.00\% & 76.67\% & 0.6124 & 0.4623 \\ \hline
7 & 50 & 00:04:09 & 88.00\% & 87.78\% & 0.387 & 0.3339 \\ \hline
8 & 60 & 00:04:53 & 92.00\% & 87.78\% & 0.2985 & 0.3339 \\ \hline
12 & 90 & 00:06:58 & 92.00\% & 90.00\% & 0.2654 & 0.2376 \\ \hline
13 & 100 & 00:07:38 & 90.00\% & 87.78\% & 0.3719 & 0.3521 \\ \hline
15 & 120 & 00:09:05 & 96.00\% & 90.00\% & 0.1376 & 0.2287 \\ \hline
19 & 150 & 00:11:10 & 94.00\% & 91.11\% & 0.2833 & 0.2157 \\ \hline
23 & 180 & 00:13:17 & 92.00\% & 92.22\% & 0.1218 & 0.1865 \\ \hline
25 & 200 & 00:14:38 & 92.00\% & 88.89\% & 0.2385 & 0.229 \\ \hline
27 & 210 & 00:15:28 & 96.00\% & 91.11\% & 0.2519 & 0.2152 \\ \hline
30 & 240 & 00:17:28 & 96.00\% & 91.11\% & 0.1583 & 0.2295 \\ \hline
32 & 250 & 00:18:06 & 92.00\% & 91.11\% & 0.0896 & 0.2295 \\ \hline
34 & 270 & 00:19:25 & 92.00\% & 93.33\% & 0.1573 & 0.205 \\ \hline
38 & 300 & 00:21:22 & 90.00\% & 87.78\% & 0.2623 & 0.2446 \\ \hline
40 & 320 & 00:22:40 & 92.00\% & 87.78\% & 0.1778 & 0.2446 \\ \hline Test accuracy 0.8889 & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: InceptionV3 model testing accuracy using augmented images. Figure 10: Xception model training and validation accuracy using augmented images.