复杂背景下小麦叶部病害图像分割方法研究

针对复杂背景下小麦叶部病害图像分割问题,以小麦条锈病、叶锈病为研究对象,提出一种结合K-means聚类、Otsu阈值法等多种方法的分割策略。主要分三个步骤将小麦病斑图像分割出来:首先,利用背景与叶片a*b*分量的差异性,采用K-means聚类分割方法,去除泥土、杂草、阴影等背景,分割出小麦植株图像;其次,利用Otsu动态阈值法进行二值化处理,并结合数学形态学运算及面积阈值法分割出带有病斑的主要小麦病害叶片图像;最后,采用K-means算法对小麦病害叶片图像进行聚类运算,最终分割出小麦病斑图像。利用该方法进行分割实验,分割准确率达到95%以上,分割效果理想,为小麦叶部病害图像分割提供了参考,也为后续的小麦病害识别和诊断提供了基础。

A segmentation method for wheat leaf images with disease in complex background

According to the properties of wheat disease (wheat stripe rust and wheat leaf rust) images in complex background, we propose a segmentation method based on the K means clustering segmentation method and the Otsu threshold algorithm. The proposed method is comprised of three main steps. Firstly, by utilizing the difference between the background and the a*b* of leaves, the k-means clustering segmentation method deletes the irrelevant background. Secondly, we use the Otsu dynamic threshold segmentation method to perform binarization and combine the mathematical morphological method with the area threshold method to separate the wheat leaf image with disease from the complex background. Finally, the K means segmentation method is used again and eventually the disease spot of the image is divided. Experimental results show that the correct extraction rate can reach 95%, and it can segment the diseased regions from the whole color image with good robustness and good accuracy, thus offering an effective method for wheat disease detection and diagnosis.