# Radish Growth Stage Recognition Based on GAN and Deep Transfer Learning
## 4 Experimental Results  
The model proposed in this paper was coded in Python 3.6 and developed on an Intel Core i7-9700U processor, with 32 GB RAM and a GeForce RTX 2080 TI GPU; the experimental results were obtained using the TensorBoard visualization tool. The performance of Inception-v3 was compared to MobileNet and three other transfer learning models (Xception, VGG-16, and VGG-19). During the training process, the variables and adjusted parameters were used as shown in Table 2. We used the \"step\" learning strategy; the basic learning rate of the other transfer learning models was set to 0.001-0.0001 and decreased every five epochs, with the factor of learning rate decaying by 0.5. DropBlock was set to 0.8 in the Inception-v3 and MobileNet models; the other transfer learning models do not have an optimized technique in the convolution process. As shown in Table 3, we compared the accuracy of different models at an image size of 128 \(\times\) 128. The training parameters were set as shown in Table 2. All the models ran 10 times. The mean and the minimum accuracies for Inception-v3 were 98.75% and 97.84%, respectively. The best accuracy for Inception-v3 achieved 99.32%, which was higher  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Variable Definitions** & **Values Inception-v3** & **Values MobileNet** & \begin{tabular}{c} **Values Other** \\ **Transfer Learning** \\ \end{tabular}  \\ \hline \multirow{4}{*}{Batch\_size} & Number of samples & & & \\  & processed for each batch of training setting & 128 & 128 & 128 \\ \cline{1-1}  & The learning rate of the initial training setting & 0.001 & 0.001 & 0.001–0.0001 \\ \cline{1-1}  & After every 10 trainings, the learning rate & 0.5 & 0.  
than the accuracy of other methods. The mean accuracies for the MobileNet, Xception, VGG-16, and VGG-19 models were 98.54%, 96.98%, 96.24%, and 95.44%, respectively. According to the experimental data, Inception-v3 achieved the best prediction result, and the MobileNet prediction accuracy was also perfect. The Inception-v3 prediction accuracy was 1.77%, 2.51%, and 3.31% higher than that of the Xception, VGG-16, and VGG-19 models, respectively. As Inception-v3 and MobileNet performed better in terms of accuracy, the experiment used the collected dataset to compare and test the five original freezing depth transfer learning models: Inception-v3 and MobileNet_0.25_128, MobileNet_0.50_128, MobileNet_0.75_128, and MobileNet_1.0_128. In order to verify the effectiveness and robustness of the above models, 75% of the experimental data were randomly divided into training sets and the remaining 25% were used for testing sets. In this paper, we used _accuracy_, _loss_, _precision_, _recall_, and _F1-score_ to evaluate the performance of these models for the detection of radish growth stages. Formulas (4)-(7) were used as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN}\times 100 \tag{5}\]  
\[Precision=\frac{TP}{TP+FP}\times 100 \tag{6}\]  
\[Recall=\frac{TP}{TP+FN}\times 100 \tag{7}\]  
\[F1\text{-}score=\frac{2\times Precision\times Recall}{Precision+Recall} \times 100 \tag{8}\]  
In these formulas, _TP_ (true positives) means that the positive class is determined as a positive class, _FP_ (false positives) means that the negative class is determined as a positive class, _FN_ (false negatives) means that the positive class is determined as a negative class, and _TN_ (true negatives) means that the negative class is determined as a negative class. The comparison of performance between Inception-v3 and MobileNet is shown in Table 4.  
To verify the performance of the Inception-v3 model proposed in this paper, we chose to evaluate precision, recall, and _F1-score_ for the identification of growth stages in cherry radish images. The identification performance verification results for Inception-v3 and MobileNet are compared in Table 4. The identification precision of Inception-v3 was 95%, the recall rate was 100%, and the _F1-score_ was 97%, all of which were higher than the data in the other four MobileNet models.