## 4 Discussion  
The experimental results of this article were compared with previous works, in which the Plant Village dataset of tomato leaf diseases was used in all deep-learning network models. Indicators related to pre-processing, transfer learning, model, classes of images, and other parameters are described in Table 7.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Preprocessing** & **ACC** & **SEN** & **SPE** & **PPV** & **F1S** & **Training Time** & **Testing Time** \\
**(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(second)** & **(second)** \\ \hline Segmented images & 99.72 & 99.69 & 99.90 & 99.49 & 99.59 & 2.75 \(\times\) 10\({}^{5}\) & 29.30 \\ Non-segmented images & 99.63 & 99.58 & 99.82 & 99.37 & 99.47 & 2.98 \(\times\) 10\({}^{5}\) & 35.27 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of time for training and testing between non-segmented images and segmented images.  
Figure 11: Classification performance comparison of tomato leaf diseases using four different deep-learning network architectures.  
Without transfer learning, the features can be extracted from convolutional layers in the network or extracted by traditional feature extraction methods. [PERSON] et al. [24] proposed the principal component analysis-whale optimization algorithm (PCA-WOA) method for extracting features and then used it in a multilayer perceptron (MLP) network. This network has one input layer, two hidden layers, and one output layer for classifying ten different tomato leaf diseases. The accuracy of the obtained classifier was 94.00%. [PERSON] et al. [28] and [PERSON] et al. [54] developed convolutional layers in the CNN architecture for feature extraction and fully connected layers for disease classification. These studies applied pre-processing and transforming images to achieve model accuracies of 98.40% and 98.49%, respectively. In addition, the RGB images were pre-processed to produce the same size and grey images. This research only focused on applying the CNN, in which features of colors, textures, and edges were extracted for training. With transfer learning, [PERSON] et al. [53] and [PERSON] et al. [31] proposed the GoogleNet architectures for classifying ten disease types. Their model accuracies were 99.39% and 99.35%, respectively.  
Compared with the above studies, we optimized the tomato leaf dataset by segmenting to extract the leaf region and the black background. It is obvious that the segmentation of the leaf images reduced the training time and increased the classification performance. In addition, compared with previous research [24, 28, 31, 53, 54] without transfer learning, our VGG-19 model applied the transfer learning method with a learning rate of 0.00001, and epochs of 300; the classification results are outstanding, particularly with PPV at 99.49%, SEN at 99.69%, FIS at 99.59%, and ACC at 99.72%, respectively.  
Finally, in Table 7 we find that the previous studies basically have the same proposed methods such as using transfer learning, ten classes, and a little bit of difference of the network model, GoogleNet [31, 53] and VGG-19 (our model). The large difference is that our research applied HSV color space for image segmentation and the two studies [31, 53] did not apply preprocessing for the image sets. The result is that the classification accuracy in our proposed method is a little bit higher, particularly the ACC value is 99.72% compared with 99.35% and 99.39%, respectively.