基于改进型模糊边缘检测的小麦病斑阈值分割算法  

Segmentation algorithm with threshold for wheat lesion based on improved fuzzy edge detection

作　　者：刁智华[1,2] 刁春迎 袁万宾 毋媛媛[1,2] Diao Zhihua;Diao Chunying;Yuan Wanbin;Wu Yuanyuan(Electric Information ＆ Engineering Department, Zhengzhou University of Light Industry, Zhengzhou 450002, China;Henan Key Lab of Information Based Electrical Appliances, Zhengzhou 450002, China)

机构地区：[1]郑州轻工业学院电气信息工程学院,郑州450002 [2]河南省信息化电器重点实验室,郑州450002

出　　处：《农业工程学报》2018年第10期147-152,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金：河南省科技厅科技攻关项目(162102110118);河南省高等学校青年骨干教师培养计划(2016GGJS-088);郑州轻工业学院研究生科技创新基金资助项目(2016040)

摘　　要：针对小麦病斑分割不准确、噪声大以及病斑边缘不清晰等问题,结合传统的作物病斑分割方法,提出一种基于改进的模糊边缘检测的图像阈值分割算法。图像预处理方面,在分析了传统模糊边缘检测缺点的同时对算法作了两个方面的改进,使用梯度倒数加权平均滤波方法去除小麦病斑噪声,然后对多层次模糊算法进行数值分层改进,增强病斑边缘信息;最后对传统的阈值分割方法进行了算法改进,采用一种改进的最大类间方差比阈值分割方法,在增强图像边缘的基础上进行阈值分割,改进阈值选取方法,在模糊增强后的小麦病斑图像上进行阈值分割提取出小麦病斑形状特征。对在大田环境下获取的小麦病害图像进行边缘增强和阈值分割试验,与传统固定阈值分割算法试验对比得出,基于改进的模糊边缘增强与阈值分割相结合的改进算法正确分割率达98.76%,相比传统固定阈值分割算法提高了8.35个百分点,漏检比增加了1.29个百分点,噪声比为1.86%,相比减少了8.36个百分点,在运算时间上减少了0.331 s,不仅突出病斑边缘信息,而且分割效率高、噪声小,可为图像分割方法的研究提供了可参考依据。Wheat is an economic crop correlated with national lifeblood, and its yield has a direct impact on people＇s living standard and economic development, while the occurrence of disease is an important cause of crop yield decline. There are many kinds of crop diseases. Timely detection of disease types and corresponding prevention and control are urgent requirements to reduce the risk of crop yield decline. The disease segmentation is the priority among priorities of disease detection, and segmentation of lesion information is a prerequisite for disease identification, discrimination of disease degree, and pesticide application decision. The picture of wheat taken under natural conditions is greatly affected by the environment. The main obstacle of image segmentation is to find interesting parts in complex background. At present, the RGB （red, green, blue） sub region component segmentation method is usually used for image segmentation, and then the results are obtained by using some merging methods, but there is a large amount of computation in the segmentation of sub region components. For the wheat lesion segmentation, there exist the problems of noise and lesion edge being not clear. The research on wheat lesion image segmentation algorithm shows that the general image segmentation method has poor adaptability and compatibility, and other methods of mixing is difficult to achieve the desired results. Fuzzy edge detection with strong adaptability is the first algorithm to solve such problems. The traditional fuzzy edge detection method is first-order differentiating the preprocessed images, and edge detection is realized by edge discontinuity. Aiming at the disadvantages of the traditional algorithm such as high error rate, easy to lose the weak edge information, an improved image threshold segmentation algorithm based on fuzzy edge detection is proposed in this paper. In the aspect of image preprocessing, after analyzing the shortcomings of the traditional fuzzy edge detection, 2 improvements have been made

关 键 词：作物 图像分割 病害 算法 边缘检测 模糊增强 小麦病斑 

分 类 号：TN911.73[电子电信—通信与信息系统]