## 4 Experimental Results and Analysis
### Main Results  
Figure 5 shows the comparative analysis of the segmentation results of the diseased leaves using our algorithm, the K-means clustering algorithm, and the mean shift clustering algorithm. The second row of Figure 5 is the result after segmentation using the K-means algorithm. As seen from the figure, the boundary of the result after segmentation is fuzzy. In addition, the segmentation accuracy of the K-means algorithm is low and easily misclassified normal leaves as lesions. The third row in Figure 5 is the segmentation result of the mean shift algorithm. As seen from the figure, the segmentation result of the mean shift algorithm has a clear boundary value, but its segmentation accuracy is relatively low, and some diseases cannot be accurately divided. In addition, the mean shift algorithm displayed over-segmentation and under-segmentation problems. The last row of Figure 5 is the segmentation result of the algorithm in this paper. It was seen that the algorithm designed in this paper had good anti-noise ability and could segment the areas of the pests and diseases more completely. Figure 5: Lesion segmentation results based on different algorithms. (**a**) Diseased leaf image. (**b**) Diseased leaf image. (**c**) Diseased leaf image. (**d**) Diseased leaf image. (**a1**) Segmentation results with the K-means method. (**b1**) Segmentation results with the K-means method. (**c1**) Segmentation results with the K-means method. (**d1**) Segmentation results with the K-means method. (**a2**) Segmentation results with the mean shift method. (**b2**) Segmentation results with the mean shift method. (**d2**) Segmentation results with the mean shift method. (**c2**) Segmentation results with the mean shift method. (**a3**) Segmentation results using our method. (**b3**) Segmentation results using our method. (**c3**) Segmentation results using our method. (**d3**) Segmentation results using our method. Table 2 compares the performance of our algorithm with the K-means and mean-shift algorithms in terms of the running time, number of iterations, and segmentation accuracy. As seen in Table 2, this algorithm has the lowest execution time and iteration time, followed by the K-means algorithm. Especially in terms of the iterations, the iteration time of our algorithm is less than half of the K-means and one-third of the mean shift algorithm. Figure 6 shows the average iterative convergence time of the three algorithms. The number of iterative convergences of our algorithm is 145, while that of the K-means algorithm is 352, and that of the mean shift algorithm is 474. Therefore, the iterative convergence rate of our algorithm is much faster than the other two algorithms. Table 3 compares the performance of our method with the FCN and U-Net segmentation algorithms based on deep learning. In the experiment, 100 diseased leaf maps were used for testing, and the segmentation results were statistically analyzed. It was seen that this algorithm is superior to other methods in every index. Figure 7 shows the segmentation results of some diseased leaf images. It was seen that FCN has the worst segmentation effect, and the diseased area could not be completely segmented, which may have led to over-segmentation and under-segmentation. U-Net could segment the contours of the diseased areas in the leaves but could not segment the boundaries of the diseased leaf. The algorithm in this paper could segment the diseased area effectively and could accurately segment the boundary and details of the diseased leaf, which fully proves the effectiveness of our method in this paper.