# Radish Growth Stage Recognition Based on GAN and Deep Transfer Learning
## 4 Experimental Results
Additionally, the recognition accuracy levels of Inception-v3, MobileNet_0.25_128, MobileNet_0.50_128, MobileNet_0.75_128, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Run NO**} & \multirow{2}{*}{**Inception-v3**} & \multirow{2}{*}{**MobileNet**} & \multicolumn{4}{c}{**Transfer Learning Models**} \\ \cline{3-6}  & & & **Xception** & **VGG-16** & **VGG-19** \\ \hline
1 & 98.73 & 98.54 & 97.4 & 96.08 & 94.94 \\
2 & 99.05 & 97.79 & 97.33 & 97.34 & 95.4 \\
3  
MobileNet_1.0_128 were 99.5%, 97.2%, 98.7%, 98.5%, and 98.9%, respectively. The loss in Inception-v3 was 0.06; for all the other models, the loss was higher than 0.10. The test accuracy and loss figures for the five models are shown in Figure 8. It can be seen from the figure that with the increase in the number of convolution kernels in MobileNet, the model can achieve higher accuracy more rapidly. However, it is worth noting that due to the small dataset, MobileNet is more prone to overfitting and oscillation, and its generalization performance is poor compared with that of Inception-v3; accordingly, the experiment was performed on Inception-v3. In order to verify the effectiveness of the improved deep transfer learning method proposed in this paper, the improved methods 1, 2, and 3 proposed in Figure 9 are compared to the original method. The experimental results from these three models on the collected dataset are shown in the figure below. It can be clearly seen from the figure that although Model 1 achieved higher accuracy more rapidly than the original model, its curve fluctuated greatly and was prone to overfitting. The proposed improved Model 2 has the same final accuracy as the original model: it achieved rapid convergence, reduced the loss to a lower level, greatly reduced the local optimal oscillation of the model, and significantly improved the model recognition performance. The improved Model 3 also performed well up to 700 steps, but its performance was not very stable in the following training, possibly due to the small dataset. The accuracy and loss for each model are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _Precision_ & _Recall_ & _F1-Score_ & _Accuracy_ & _Loss_ \\ \hline Inception-v3 & 95\% & 96\% & **97\%** & **99.5\%** & **0.06** \\ MobileNet\_0.25\_128 & 88\% & 91\% & 89\% & 97.2\% & 0.35 \\ MobileNet\_0.50\_128 & 92\% & 88\% & 89\% & 98.7\% & 0.24 \\ MobileNet\_0.75\_128 & 94\% & 93\% & 93\% & 98.5\% & 0.22 \\ MobileNet\_1.0\_128 & 78\% & 89\% & 83\% & 98.9\% & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision, recall, F1-score, and accuracy results for Inception-V3 and MobileNet models. Figure 8: (a) Accuracy for the test set of five models. (b) Loss for the test set of four models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Mean Accuracy** & **Mean Loss** \\ \hline Original model & **99.5\%** & 0.06 \\ Proposed Model 1 & 98.9\% & 0.03 \\ Proposed Model 2 & **99.5\%** & **0.01** \\ Proposed Model 3 & 99.0\% & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results based on our data. The Oxford Flower dataset was used to further test the universal applicability of the model proposed in this paper. The testing dataset contained five types of flowers, and included 633 daisies, 898 dandelions, 641 roses, 699 sunflowers, and 799 tulips. Example images are shown in Figure 10.  
The experimental results are shown in Figure 11. The figures show that the original model converged slowly and produced large oscillations. Among the four models, proposed Models 2 and 3 still performed very well, both for accuracy and loss. The consistency of the experimental results demonstrates the rationality of the dataset produced in this study. Table 6 shows the specific data for accuracy and loss, from which we can see that proposed Model 3 has the highest identification accuracy (99.3%) and the lowest identification loss (0.02).