基于K-means聚类算法的叶螨图像分割与识别

A method for image segmentation and recognition of spider mites based on K-means clustering algorithm

【目的】叶螨（spider mite）是为害多种农作物的主要害虫,叶螨识别传统方法依靠肉眼,比较费时费力,为研究快速自动识别方法,引入计算机图像分析算法。【方法】该方法基于K-means聚类算法对田间作物上的叶螨图像进行分割与识别。【结果】对比传统RGB彩色分割方法,K-means聚类算法能够有效地对叶片上叶螨图像进行分割和识别。K-means聚类算法平均识别时间为3.56 s,平均识别准确率93.95%。识别时间T随图像总像素Pi的增加而增加。【结论】K-means聚类组合算法能够应用于叶螨图像分割与识别。

[ Aim ] The spider mites are the main pests of many crops. Traditional recognition methods for spider mites relied on the naked eyes, which wasted a lot of time and energy. In order to study the fast automatic recognition method for spider mites, a method using computer image analysis algorithm was developed. [ Methods ] The method based on the K-means clustering algorithm realized the segmentation and recognition of the spider mite images which were obtained from fields. [ Results ] In contrast to the traditional RGB color segmentation method, the K-means clustering algorithm method was able to separate the images of spider mites from leaf background effectively. The average recognition time based on the K- means clustering algorithm was 3.56 s, and the recognition accuracy was 93.95 %. The recognition time （T） increased as the pixels of tested image （Pi） increased. [ Conclusion ] The method can be applied to the segmentation and recognition of spider mite images.