CAJ | 학술논문

将图像分析与模式识别技术应用于小麦腥黑穗病害图像的分类诊断,对于提高出入境植物病害检验检疫工作的自动化程度具有实际意义。文中在分析了小麦网腥、印度腥和矮腥等3种病害孢子图像的形状和纹理特征后,选择了描述孢子的长轴、短轴、等价椭圆短轴、面积、周长及惯性矩等6个典型特征,设计了一个具有6个输入向量、4个输出向量的BP神经网络小麦病害分类器,用于对这3种病害图像进行分类诊断。经初步试验,对33个测试样本的正确识别率达到81.8%,表明该分类器具有较高的精度,能够完成这3种病害的分类诊断任务
장도상분석여모식식별기술응용우소맥성흑수병해도상적분류진단,대우제고출입경식물병해검험검역공작적자동화정도구유실제의의。문중재분석료소맥망성、인도성화왜성등3충병해포자도상적형상화문리특정후,선택료묘술포자적장축、단축、등개타원단축、면적、주장급관성구등6개전형특정,설계료일개구유6개수입향량、4개수출향량적BP신경망락소맥병해분류기,용우대저3충병해도상진행분류진단。경초보시험,대33개측시양본적정학식별솔체도81.8%,표명해분류기구유교고적정도,능구완성저3충병해적분류진단임무
Application of the techniques of image analysis and pattern recognition to the sorted diagnose of Tilletia disease is of significance to improving the automation of detecting the diseases of entry-exit plants.After an analysis of the shapes and texture features of the spore images of Tilletia caries（DC.） Tul.,Tilletia indica Mitra and Tilletia controversa Kühn,this paper selects six typical features including major axis,minor axis,minor axis of equivalent ellipse,area,perimeter and moment of inertia to describe the spore region.Next,a classifier for wheat disease based on the BP neural network consisting of 6 input vectors and 4 output vectors is designed for the classification of the three diseases’ images.The primary tests,in which the accuracy of recognition accuracy towards 33 samples is up to 81.8%,shows high accuracy and capability of the classification of the three diseases