# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
## 3 Results
### Parameter Adjustment Experiment  
The first test is the proposed grape leaf diseases identification network based on the Fourier domain adaptation with a single scale on the source domain: 956 images of each category \(\rightarrow\) target domain; 239 images of each category. In order to experiment with better performance of the model, we choose different hyper-parameter configurations (learning rate, batch size, and epochs) to adjust the identification performance of the model. The learning rate is selected as 0.0001, 0.0005, 0.001, and 0.01, the batch size is selected as 4, 8, 16, and 32, and the epoch is selected as 50, 100, and 200. The experiment is divided into two parts. Firstly, two groups with higher accuracy are selected through the combination of learning rate and batch size. They are a learning rate of 0.005 and batch size 8 as well as a learning rate of 0.01 and batch size 4. All networks use Alexnet, shown as in Table 3. Then, we used these two sets of parameters to train on the three classical networks of CNN, combined with epochs selected as 50, 100, and 200. In addition, three kinds of \(\beta\) were selected for experiments.  
When \(\beta\) is selected as 0.01, 0.05, and 0.09, the experimental process is shown in Tables 4-6. After 54 experiments, the final results are as follows: when \(\beta\) = 0.01, Vgg13 network is selected for training, epochs = 100, learning rate = 0.0005, epoch = 8, and the accuracy is the highest, which can reach 96.2%. When \(\beta\) = 0.05, select the Vgg13 network for training, set epochs = 200, learning rate = 0.0005, epoch selection 8, and the highest accuracy can also reach 96.2%. When \(\beta\) = 0.09, the Vgg13 network is selected for training. When epochs = 100, learning rate = 0.01, epoch 4 is selected, the accuracy is the highest, and the highest accuracy can be achieved, which is 96.7%.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Learning Rate** & **Batch\_Size** & **Accuracy** \\ \hline \multirow{4}{*}{0.0001} & 4 & 65\% \\  & 8 & 45.5\% \\  & 16 & 27.0\% \\  & 32 & 33.3\% \\  & 4 & 79.8\% \\  & 8 & 88.1\% \\  & 16 & 65.4\% \\  & 32 & 60.4\% \\  & 4 & 81.9\% \\  & 8 & 78.7\% \\  & 16 & 76.4\% \\  & 32 & 65.7\% \\  & 4 & 93.2\% \\  & 8 & 91.4\% \\  & 16 & 92.6\% \\  & 32 & 91.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter adjustment experiment.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Learning Rate** & **Epochs** & **Models** & **Accuracy** \\ \hline \multirow{8}{*}{0.0005} & \multirow{8}{*}{100} & Alexnet & 91.1\% \\  & & Vgg13 & 95.1\% \\  & & Resnet101 & 90.8\% \\  & & Alexnet & 88.1\% \\  & & Veg13 & 96.2\% \\  & & Resnet101 & 83.8\% \\  & & Alexnet & 92.1\% \\  & 200 & Veg13 & 95.5\% \\  & & Resnet101 & 84.5\% \\  & & Alexnet & 94.7\% \\  & 50 & Veg13 & 95.5\% \\  & & Resnet101 & 90.7\% \\  & & Alexnet & 93.2\% \\
0.01 & 100 & Veg13 & 95.5\% \\  & & Resnet101 & 89.0\% \\  & & Alexnet & 92.5\% \\  & 200 & Veg13 & 93.6\% \\  & & Resnet101 & 89.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental process when \(\beta\) = 0.01.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Learning Rate** & **Epochs** & **Models** & **Accuracy** \\ \hline \multirow{8}{*}{0.0005} & \multirow{8}{*}{100} & Alexnet & 90.9\% \\  & & Vgg13 & 95.8\% \\  & & Resnet101 & 90.8\% \\  & & Alexnet & 88.1\% \\  & 100 & Veg13 & 96.2\% \\  & & Resnet101 & 83.8\% \\  & 200 & Veg13 & 95.5\% \\  & 200 & Veg13 & 95.5\% \\  & 200 & Veg13 & 95.5\% \\  & 50 & Veg13 & 95.7\% \\  & 50 & Resnet101 & 87.4\% \\  & & Alexnet & 91.6\% \\
0.01 & 100 & Veg13 & 93.4\% \\  & & Resnet101 & 90.1\% \\  & & Alexnet & 91.8\% \\  & 200 & Veg13 & 93.4\% \\  & 200 & Veg13 & 93.4\% \\  & & Resnet101 & 90.1\% \\  & & Alexnet & 91.8\% \\  & 200 & Veg13 & 93.4\% \\  & & Resnet101 & 88.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The experimental process when \(\beta\) = 0.05.