## 2 Related Works  
Various image segmentation and clustering-based methods were used for detecting the regions of crop diseases. [PERSON] et al. [9] used a method of diseased leaf image segmentation based on hybrid clustering, which divides the image into uniform superpixels through superpixel clustering to guide the rapid convergence of the algorithm. [PERSON] et al. [10] designed a segmentation method for crop lesion leaves with a complex background in which the optimal segmentation threshold could be obtained by using a weighted Parson window. [PERSON] et al. [11] introduced a scheme for the automatic detection and classification of plant leaf diseases, which used specific thresholds to mask and delete green pixels and effectively segment the images based on texture statistics. [PERSON] et al. [12] designed a disease detection system for color soybean leaf images based on complex backgrounds. Based on the K-means algorithm and combined with empirical thresholds, a bump map was used to segment the salient regions from soybean leaf disease images. [PERSON] et al. [13] built a content image database retrieval system, which used a canny edge detection operator and histogram analysis to identify the leaf diseases of medicinal plants. This method highlighted the regions with high spatial derivatives by searching for image gradients and finally detected the diseases according to the edge features of each medicinal plant leaf. [PERSON] et al. [14] explored the influence of different segmentation thresholds on the detection of diseased oat and sunflower leaves. [PERSON] et al. [15] constructed a database of rice leaves in a simple background, then used the K-means clustering method to extract the diseased parts in the leaf image, and used threshold technology to remove the unnecessary green areas of the diseased parts. [PERSON] et al. [16] designed a leaf lesion image segmentation algorithm that first recognized the greenest pixels, which were masked by the specific thresholds calculated by the Otsu segmentation method, and the zero values of red, green, and blue and pixels on the border to completely remove the infected cluster. [PERSON] et al. [17] applied a color image K-means clustering algorithm to grape leaf image segmentation. [PERSON] et al. [18] designed a watershed algorithm for crop disease leaf segmentation. [PERSON] et al. [19] designed a segmentation algorithm for diseased spots using maximum expectation (EM) processing technology and compared the effects of CIELAB, HSI, and YCbCr color spaces in the detection of disease spots. [PERSON] et al. [20] improved the traditional Sobel operator. Based on the preprocessing of graying, filtering, and noise reduction of the leaf image, the Sobel operator was improved by adding multiple directional templates, which effectively solved the problem that the boundary points of the traditional Sobel operator were too rough during the edge detection. [PERSON] et al. [21] designed a multi-scale analysis algorithm for the edge extraction of strawberry disease leaf images. This method first used wavelet functions to reconstruct leaf images at different scales, then used the Otsu algorithm to obtain the binary image regions of the reconstructed images at different scales, used the Canny operator to obtain the diseased edge part in the image, and obtained the complete area of the leaf lesion through a spatial fusion of different scale images. [PERSON] et al. [22] designed a two-stage segmentation model that includes both normal and absolute segmentations. Fast segmentation is realized with ordinary segmentation, and good segmentation accuracy is achieved with absolute segmentation. Many scholars have tried to use CNNs to solve the problem of plant disease image segmentation. [PERSON] et al. [23] combined Markov and CNNs to complete the segmentation of cotton spots.