# Buckwheat Disease Recognition Based on Convolution Neural Network
## 5 Results
### The Recognition of Buckwheat Diseases  
The inception structure of Section 4.2.1 and the CNN with cosine similarity convolution were adopted to recognize buckwheat disease. We compared the final recognition accuracy and used Precision, Recall, and F1-measure to evaluate the recognition effect [36]. The experimental results are shown in Table 3, in addition to the comparison with mainstream CNN models (AlexNet, VGG, GoogleNet, ResNet, LeNet, Faster R-CNN, R-FCN, FPN, YOLOv3, ZFNet) [36], in order to demonstrate the performance of the proposed method, we also introduced the face recognition model, which is currently well studied to test in data set of the buckwheat disease. The results show that accuracy, precision, recall, and F1-measure reached 96.43, 96.82, 95.62, and 96.71%, respectively, for our method. Compared with AlexNet, VGG, GoogleNet, ResNet and Faster R-CNN, R-FCN, YOLOv3 LeNet, ZFNet, the optimal performance of our method was improved by 1.47, 2.1, 1.17, and 3.03%, respectively. The FPS (average number of pictures processed per second) of our method is 5.19, which shows that its processing speed is also at a high level. We selected some models to draw ROC curves on validation sets and test sets, as follows in Figure 18. It can be seen that ROC curves on the test set and validation set are basically consistent; therefore, our model is stable and performance is optimal. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** & **AUC (\%)** & **FPS (s)** \\ \hline AlexNet & 87.31 & 84.92 & 88.57 & 90.34 & 96.13 & 3.15 \\ Vgg-16 & 89.27 & 88.42 & 90.35 & 89.53 & 94.12 & 3.18 \\ GoogleNet & 90.53 & 92.84 & 91.47 & 90.53 & 95.35 & 4.15 \\ ResNet & 94.75 & 93.72 & 92.37 & 93.41 & 97.18 & 4.17 \\ Faster R-CNN & 93.24 & 92.34 & 93.72 & 91.58 & 95.36 & 5.37 \\ R-FCN & 94.22 & 94.31 & 94.36 & 92.15 & 95.31 & 4.85 \\ FPN & 94.16 & 94.83 & 92.51 & 93.87 & 97.78 & 4.27 \\ YOLOv3 & 95.03 & 94.72 & 94.51 & 92.67 & 96.46 & 4.53 \\ LeNet & 94.57 & 94.23 & 92.59 & 91.88 & 95.12 & 5.21 \\ ZFNet & 94.21 & 94.17 & 93.23 & 93.42 & 97.43 & 5.33 \\
**Ours (inception +Cosine similarity convolution)** & **96.43** & **96.82** & **95.62** & **96.71** & **98.21** & **5.19** \\ \hline DeepFace & 91.53 & 90.31 & 90.73 & 89.35 & 94.37 & 5.18 \\ VGGFace & 91.82 & 91.24 & 89.42 & 90.17 & 95.52 & 5.16 \\ FaceNet & 90.47 & 89.27 & 90.33 & 90.16 & 96.29 & 4.21 \\ DeepID2+ & 93.82 & 93.57 & 92.49 & 90.38 & 96.17 & 4.17 \\ WST Fusion & 92.73 & 92.47 & 93.21 & 92.54 & 97.05 & 3.18 \\ SphereFace & 93.68 & 94.38 & 92.86 & 92.67 & 97.46 & 3.23 \\ RangeLoss & 91.79 & 92.45 & 91.65 & 90.78 & 95.37 & 3.21 \\ HiReST-9+ & 88.36 & 88.67 & 89.59 & 89.76 & 94.88 & 5.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of buckwheat disease identification. Figure 17: Convolution feature map of buckwheat leaf under uneven illumination. In order to further study the model effect for buckwheat disease recognition after the disease areas of buckwheat leaf are detected, we tested whether to perform area detection. After the method of disease area detection was added in Section 4.1, the recognition effect was significantly improved, as shown in Table 4. After the method of disease area detection was adopted, the accuracy, precision, recall, and F1-measure of our method reached 98.13, 97.54, 96.38, and 97.82%, respectively, which was an increase of 1.7, 0.72, 1.76, and 1.11% compared without disease area detection. Combined with the analysis of Table 3 it is found that after the disease area detection is adopted, the average accuracy precision, recall, and F1-measure of each recognition model increased by 1.3, 1.51, 1.69, and 1.48%, respectively. However, we found that area detection introduces additional computational overhead, so the processing speed is slightly reduced. The PFS of all models decreased by an average of 0.46, but it has little effect on the overall performance.