## 5 Discussions and Conclusions  
As seen in Figure 5, compared with other methods, the algorithm designed in this paper has good anti-noise performance and higher segmentation accuracy, which could better segment diseased leaf regions. The K-means algorithm only considered the color similarity calculated with the Euclidean distance between the pixels, not the spatial relationship between the pixels, so it easily misclassiffied normal leaves as lesions. The mean shift algorithm had a particular anti-noise performance and higher segmentation accuracy, but only with appropriate bandwidth. The algorithm designed in this paper fully discussed the relationship between the pixels, considering not only the color information and spatial information between the pixels but also the pixel number (density); therefore, the segmentation effect is better than the other methods. In addition, Table 2 shows that our algorithm converges faster than the other methods. The K-means clustering algorithm needs to specify the number of clusters and initial cluster center manually, and the whole segmentation process is time-consuming. The mean shift clustering algorithm needs to select a bandwidth value, which requires manual participation. The algorithm designed in this paper has certain adaptability, may automatically select the number of clusters and the initial cluster center, and greatly speeds up the convergence of the algorithm. Compared with the methods based on depth learning, the algorithm designed in this paper has a faster convergence speed and higher segmentation accuracy. The FCN and U-Net methods have high requirements for datasets. More data could improve the performance of the model. At the same time, due to a large number of model parameters, our method is better than the two methods in the prediction stage. The method in this paper overcomes the shortcomings of the traditional K-means clustering algorithm and mean shift clustering algorithm and could automatically determine the cluster center and the number of clusters. It could also comprehensively consider the color information, spatial information, and density information between the pixels. This method could automatically, quickly, and accurately complete the fast segmentation of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Execution Time (s)** & **SA(\%)** & **OSR(\%)** & **USR(\%)** \\ \hline FCN & 11.49 & 61.34 & 37.36 & 38.66 \\ U-Net & 9.63 & 73.55 & 30.14 & 26.45 \\ Our method & 5.76 & 81.21 & 20.78 & 18.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm performance comparisons. Figure 7: Lesion segmentation results based on different algorithms. diseased leaves and obtain high segmentation accuracy. In future research, work may focus on the deployment of algorithms by deploying different algorithms to hardware devices to check the pros and cons of performance. In addition, the pictures collected with the hardware would contain rich backgrounds, and how to accurately segment the pest and diseased areas from leaves with complex backgrounds is also the next research focus. Conceptualization, Z.L. ; methodology, Y.R. ; software, Q.L. and Z.L. ; validation, Q.L. ; writing--original draft preparation, Y.R. All authors have read and agreed to the published version of the manuscript. This work was partly supported by the College Research Funding of Xijing University (XJ170128) and the Key Development Projects of Shaanxi Science and Technology Department (2017 ZDDM-NY-088). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper. All data included in this study are available upon request by contacting the corresponding author. The authors declare no conflict of interest. * [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON]; [PERSON] Color transform based approach for disease spot detection on plant leaf. _Int. J. Comput. Sci. Telecommun._**2012**, \(3\), 65-70.