# Buckwheat Disease Recognition Based on Convolution Neural Network
## 6 Conclusions and Future Directions  
In the article, the main contribution is to realize accurate disease area detection by MSER and CNN, and a two-level inception structure was added to improve classification accuracy. Furthermore, in order to eliminate the interference in disease identification, which is caused by illumination imbalance, cosine similarity is introduced in the convolution process. In this work, the CNN structure based on inception combined with cosine similarity was better than the current recognition framework in the performance for buckwheat diseases. Compared with recognition frameworks, such as the AlexNet, VGG, GoogleNet, ResNet et al., the accuracy, precision, recall, and F1-measure, our method got a consistent outperformance regarding precision, recall, and F1-score. In particular, the proposed method is robust when the light is not uniform and the leaves are crossed and overlapped. Because our method detects the disease area, it results in a small increase in processing time, but overall performance is not affected. Due to the similar symptoms of buckwheat downy mildew and buckwheat Seedling blight, the accuracy of recognition is not high. In future plans, we will further improve the identification accuracy of these two diseases.  
[PERSON] completed theoretical analysis and model design; [PERSON] completed the data analysis; [PERSON] carried out implementation and data tests on the model; [PERSON] provides basic data and disease identification; [PERSON] completed the program; [PERSON] sorted out the papers and completed the drawings. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NSFC Grant Nos. 61701060 and 61801067, Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Project No. GIIP1806, and the Science and Technology Research Project of Higher Education of Hebei  
Figure 21: Buckwheat disease classification; (**a**) confusion matrix, (**b**) normalized confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Disease Type** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Measure (\%)** & **AUC (\%)** \\ \hline Spot blight & 90.37 & 90.51 & 91.35 & 92.92 & 96.78 \\ Sclerotinia & 83.53 & 82.48 & 83.41 & 80.54 & 87.32 \\ Seedling blight & 79.18 & 78.38 & 80.72 & 80.21 & 86.75 \\ Ring spot & 91.43 & 91.57 & 92.61 & 92.39 & 96.41 \\ Downy mildew & 78.31 & 78.45 & 75.28 & 77.38 & 82.13 \\ Brown spot & 86.93 & 85.91 & 88.47 & 86.52 & 89.57 \\ Virus disease & 87.42 & 86.19 & 87.43 & 86.52 & 88.46 \\ White mold & 85.48 & 85.36 & 86.92 & 86.74 & 87.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of different buckwheat diseases for their identification.  
Province (Grant No. QN2019069), and Chongqing Key Lab of Computer Network and Communication Technology (CY-CNCL-2017-02).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to acknowledge the support for our work from the researchers of Chongqing Key Lab of Computer Network and Communication Technology.  
**Conflicts of Interest:** There are no conflicts of interest in this work.