# Buckwheat Disease Recognition Based on Convolution Neural Network
## 5 Results
### The Recognition of Buckwheat Diseases
To verify the advantages of our structure, the cosine similarity convolution was added to ResNet, Faster R-CNN, R-FCN,  
Figure 18: ROC curve for different models on validation sets and test sets. (**a**) ROC curve for different models on testing sets; (**b**) ROC curve for different models on validation sets. FPN, YOLOv3, LeNet, and ZFNet. The results are shown in Figure 19. It can be seen that the structure presented in this paper has a better effect on the discrimination of buckwheat diseases compared with other networks. ROC curves on validation sets and test sets, as shown in Figure 20. The model performance is similar in the validation set and test set. In fact, the effect on the test is a little more than the effect on the validation set. We also classified buckwheat spot blight, buckwheat sclerotinia, buckwheat seedling blight, buckwheat ring spot, buckwheat downy mildew, buckwheat brown spot, buckwheat virus disease, and buckwheat white mold. Table 5 shows the results of specific disease classification using the identification framework proposed in this article after the disease area detection. It can be seen that the recognition effect for specific diseases has been significantly reduced, especially for buckwheat downy mildew. Because it is a binary classification problem to discriminate whether there are diseases in buckwheat, it is simply, the classification effect is better. If we want to identify specific disease types, the difficulty of identification will increase as the number of classification targets increases. Due to the different performance of the photography equipment and different photography environments, the imaging quality is quite different; Moreover, the accuracy of recognition largely \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** & **AUC (\%)** & **FPS (s)** \\ \hline AlexNet & 87.93 & 86.37 & 88.92 & 92.34 & 97.12 & 3.12 \\ Vgg-16 & 90.17 & 90.17 & 91.83 & 90.27 & 95.73 & 2.87 \\ GoogleNet & 93.42 & 94.21 & 93.84 & 92.67 & 96.59 & 3.87 \\ ResNet & 93.78 & 94.92 & 94.42 & 94.89 & 97.37 & 3.72 \\ Faster R-CNN & 95.16 & 95.25 & 94.57 & 92.54 & 96.86 & 3.81 \\ R-FCN & 95.26 & 96.13 & 95.38 & 92.87 & 97.12 & 3.85 \\ FPN & 96.37 & 96.21 & 94.36 & 95.17 & 97.84 & 3.57 \\ YOLOv3 & 95.57 & 95.32 & 96.83 & 95.37 & 98.03 & 4.12 \\ LeNet & 94.87 & 95.17 & 93.27 & 92.68 & 95.68 & 4.27 \\ ZFNet & 95.78 & 94.35 & 95.12 & 93.71 & 96.73 & 4.03 \\
**Ours (inception + Cosine similarity convolution)** & **98.13** & **97.54** & **97.38** & **97.82** & **98.89** & **4.31** \\ \hline DeepFace & 94.73 & 93.28 & 92.67 & 90.48 & 95.42 & 4.43 \\ VGGFace & 93.02 & 92.83 & 91.61 & 92.35 & 96.74 & 4.32 \\ FaceNet & 92.57 & 91.67 & 92.57 & 92.67 & 96.81 & 3.49 \\ DeepID2+ & 94.24 & 94.38 & 93.87 & 92.49 & 97.13 & 3.53 \\ WST Fusion & 93.86 & 94.35 & 95.61 & 93.49 & 97.41 & 3.02 \\ SphereFace & 94.56 & 94.85 & 93.72 & 93.57 & 97.58 & 3.13 \\ RangeLoss & 92.71 & 93.73 & 92.71 & 92.18 & 96.93 & 2.97 \\ HiReST-9+ & 90.73 & 90.32 & 91.49 & 91.33 & 95.93 & 4.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Identification results of Buckwheat diseases after region detection. Figure 19: The cosine similarity convolution was added to other networks. depends on the training of the model and the number of samples. The samples in this article comes from field collection and is limited by conditions. There are only 500 samples of each disease, and the model training is insufficient, so the recognition effect is greatly affected. However, it can be seen that the accuracy, precision, recall, and F1-measure of buckwheat spot blight and buckwheat ring disease still reach more than 90%. This is because the edge contours of these two diseases on buckwheat leaves are clear. The feature map of the disease can be obtained accurately when the convolutional neural network is used for feature extraction, so it shows higher accuracy in classification.