###### Abstract  
Plant diseases and pests may seriously affect the yield of crops and even threaten the survival of human beings. The characteristics of plant diseases and insect pests are mainly reflected in the occurrence of lesions on crop leaves. Machine vision disease detection is of great significance for the early detection and prevention of plant diseases and insect pests. A fast detection method for lesions based on a single-channel gravitational kernel density clustering algorithm was designed to examine the complexity and ambiguity of diseased leaf images. Firstly, a polynomial was used to fit the R-channel feature histogram curve of a diseased leaf image in the RGB color space, and then the peak point and peak area of the fitted feature histogram curve were determined according to the derivative attribute. Secondly, the cluster numbers and the initial cluster center of the diseased leaf images were determined according to the peak area and peak point. Thirdly, according to the clustering center of the preliminarily determined diseased leaf images, the single-channel gravity kernel density clustering algorithm in this paper was used to achieve the rapid segmentation of the diseased leaf lesions. Finally, the experimental results showed that our method could segment the lesions quickly and accurately.  
gravitational kernel density; clustering algorithm; image segmentation; color space Article  
[PERSON], [PERSON], [PERSON], Z. The Fast Detection of Crop Disease Leaves Based on Single-Channel Gravitational Kernel Density Clustering  
using the global threshold segmentation algorithm, so a multi-threshold segmentation algorithm is needed to solve this problem. However, multiple thresholds are difficult to determine automatically with algorithms and often need to be supplemented with human participation, which renders the algorithm non-universal and unintelligent. When the leaf lesions are segmented using image clustering algorithms, the number of class families needs to be determined manually in advance, which renders the algorithm poorly adaptable and easily leads to segmentation errors. In addition, with the development of deep learning techniques, deep learning-based methods have also been applied to the field of crop disease detection. The existing architectures have also been used to extract the features and combine them with other models. However, these deep models are difficult to apply to actual scenarios due to the single content of segmentation, high network complexity, and long training time.  
In order to solve these problems, we propose a fast detection method for crop disease leaves based on single-channel gravitational kernel density clustering. Firstly, the R-channel values of the diseased leaf images were extracted in the RGB color space. Then a polynomial was used to fit the R-channel feature histogram curve. The cluster number and initial cluster center of the diseased leaf images were determined by locating the peak value and peak area. Finally, according to the initially determined clustering centers of the diseased leaf images, the gravitational kernel density clustering algorithm was used to complete the segmentation of the lesions on the diseased leaves. The experimental results showed that this method could segment the lesions quickly and accurately.