## 3 Results and Discussion
### Overall Performance of Proposed Model for Root Rot Disease Identification in Ginsing Plants  
The plant regions were initially segmented from the healthy and diseased plant images as shown in Figures 7 and 8, respectively. The time taken for the proposed segmentation method was less than 5 s per image. After segmenting the plant region, the root rot disease identification was performed by transfer learning models and proposed model. Sample plant images that were correctly identified by proposed model are shown in Figures 9 and 10. These figures show that the leaves and roots of the healthy and diseased plants have a major difference in their appearance. It can be observed from these figures that the diseased plants have shorter primary roots, whereas the length of the primary roots in the healthy plants is greater than that in the diseased plants. Furthermore, it can be observed that there are many secondary and tertiary roots in healthy plants, but there are no such roots in the diseased plants. The healthy and root-rot-diseased plants were identified in a promising manner by proposed model. This shows the performance of the proposed model in identifying root rot disease in ginseng plants. Figure 8: Plant segmentation from diseased plant images. The top row presents raw plant images; the bottom row presents segmented plant images. Figure 10: Correct identification of diseased plants by proposed model. (**a**) Raw plant; (**b**) Segmented plant region; (**c**) Plant root. Figure 9: Correct identification of healthy plants by proposed model. (**a**) Raw plant; (**b**) Segmented plant region; (**c**) Plant root. \begin{table}
\begin{tabular}{c c} \hline \hline
**Deep Learning Model** & **Training Time** \\ \hline Alexnet & 25 min 46 s \\ VGG19 & 54 min 32 s \\ Squeezenet & 7 min 16 s \\ Darknet19 & 50 min 57 s \\ Resnet18 & 12 min 51 s \\ Resnet101 & 94 min 35 s \\ Proposed model & 18 min 11 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training times of various deep learning models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Deep Learning Model** & **Precision** & **Recall** & \(\mathbf{F_{1}}\) **Score** & **Accuracy** \\ \hline Alexnet & 0.60 & 0.87 & 0.71 & 0.64 \\ VGG19 & 0.64 & 0.91 & 0.75 & 0.69 \\ Squeezenet & 0.64 & 0.93 & 0.76 & 0.71 \\ Darknet19 & 0.64 & 0.93 & 0.76 & 0.71 \\ Resnet18 & 0.66 & 0.95 & 0.78 & 0.73 \\ Resnet101 & 0.63 & 0.89 & 0.74 & 0.68 \\ Proposed model & 0.64 & 0.92 & 0.76 & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of various deep learning models with raw plant images (from testing dataset) as input. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Deep Learning Model** & **Precision** & **Recall** & \(\mathbf{F_{1}}\) **Score** & **Accuracy** \\ \hline Alexnet & 0.89 & 0.82 & 0.85 & 0.86 \\ VGG19 & 0.87 & 0.84 & 0.85 & 0.85 \\ Squeezenet & 0.89 & 0.77 & 0.83 & 0.84 \\ Darknet19 & 0.89 & 0.77 & 0.83 & 0.84 \\ Resnet18 & 0.88 & 0.84 & 0.86 & 0.86 \\ Resnet101 & 0.88 & 0.78 & 0.83 & 0.84 \\ Proposed model & 0.95 & 0.82 & 0.88 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison of various deep learning models with segmented plant regions (from testing dataset) as input images. \begin{table}
\begin{tabular}{c c} \hline \hline
**Deep Learning Model** & **Parameters** \\ \hline Alexnet & 61 M \\ VGG19 & 138 M \\ Squeezenet & 12 M \\ Darknet19 & 22 M \\ Resnet18 & 11 M \\ Resnet101 & 44 M \\ Proposed model & 6 M \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Inference times (for testing dataset) of various deep learning models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Deep Learning Model** & **Precision Time** \\ \hline Alexnet & 13.15 s \\ VGG19 & 21.63 s \\ Squeezenet & 14.41 s \\ Darknet19 & 19.45 s \\ Resnet18 & 17.39 s \\ Resnet101 & 35.93 s \\ Proposed model & 8.97 s \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Parameters of various deep learning models. The proposed model achieved a high precision value of 0.95 and a recall value of 0.82.