## 3 Results and Discussion
### Data Analysis  
Among the four networks in this experiment, the reason why VGG19 and AlexNet networks could not improve the classification accuracy in the face of training using data-augmentation data sets lay in their excessive convolution layers. In the experiment on soybean binary classification, due to the principle of the convolution neural network (Figure 10), too many convolution layers will make the noise in the image participate in the convolution training as image characteristics, so the final classification accuracy cannot achieve the expected effect. Through the above experiments, it can be seen that too many convolution layers will reduce the classification accuracy, whereas a few convolution layers can achieve better classification results. To demonstrate this view, a low convolution layer neural network, Simple (Figure 11), with four convolution layers, two pooling layers, and two fully connected layers was reconstructed. The network structure extracted image features through the convolution layer, reduced parameters through the pooling layer, and reduced the number of channels from 512 to 256 compared with VGG neural network model. Finally, images were classified and recognized through two fully connected layers. Figure 10: Schematic diagram of convolutional neural network. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **CNN** \\ **Model** \\ \end{tabular} } & \multicolumn{2}{c}{**CE Loss Function**} & \multicolumn{2}{c}{**Focal-Loss Function**} & \multicolumn{2}{c}{**Oversampling**} \\ \cline{2-7}  & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\  & **Accuracy/\%** & **Accuracy/\%** & **Accuracy/\%** & **Accuracy/\%** & **Accuracy/\%** & **Accuracy/\%** \\ \hline AlexNet & 85.99 & 82.58 & 88.60 & 87.87 & 56.30 & 50.72 \\ VGG & 89.90 & 87.87 & 89.58 & 89.39 & 52.26 & 48.55 \\ ResNet & 90.55 & 90.15 & 96.41 & 95.45 & 89.27 & 79.71 \\ DenseNet & 96.74 & 96.21 & 99.35 & 98.48 & 97.83 & 97.71 \\ \hline Average & \multirow{2}{*}{90.80} & \multirow{2}{*}{89.20} & \multirow{2}{*}{93.49} & \multirow{2}{*}{92.80} & \multirow{2}{*}{73.92} & \multirow{2}{*}{69.17} \\ value & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of network training. Figure 12 shows the convolutional network classification accuracy training, using a learning rate of 0.000002 in the training, training 100 times. The accuracy of classification training could reach 90.2% with data-augmentation data set training, 90.9% with the focal-loss function, and 88.6% with the cross-entropy-loss function. The experiment showed that the neural network with a low convolution layer could improve the classification accuracy by using the data-augmentation data set processing method and the focal-loss function in the soybean binary classification experiment. In addition, the classification accuracy of ResNet and DenseNet networks was less affected by the data-augmentation data set. According to the experiments, the low convolution layer could achieve the desired effect; it was shown that when the soybean binary classification method used the data-augmentation data set, it was necessary to retain certain original image feature data to make the network have less impact on the sampled data set. The ResNet network uses the special structure of residual blocks (shown in Figure 13), x is the input value, and F(x) + x is the output value of this layer, which can combine the original data with the data processed by the hidden layer as the data of this layer. Thus, the residual blocks can retain the original input data and reduce the impact due to noise. Therefore, using the structure of residual blocks can protect the original features of the image, so ResNet is less affected by the data-augmentation data set and still maintains a high classification accuracy. Figure 11: Schematic diagram of the Simple network structure. Figure 12: The curve of training accuracy of the convolutional network. Figure 13: Schematic diagram of the residual block.