CAJ | 학술논문

为了准确识别小麦叶部常见病害，为小麦病情诊断和发展状况判断提供科学依据，设计并实现了一种基于SVM （ Support Vector Machine ）的小麦叶部常见病害识别方法。该方法可以实现对小麦白粉病、条锈病和叶锈病的准确识别。首先，基于中值滤波法和K均值聚类算法，实现了图像的去噪及病斑分割；然后，提取了病斑区域形状特征和纹理特征；最后，利用SVM 算法对小麦叶部病害进行了分类识别。随机试验结果表明，利用所提取的特征可以有效地实现小麦叶部常见病害的识别，基于形状特征的综合识别率可达99．33％；利用 SVM 算法进行小麦病害叶片识别是有效的、可行的。该方法对于农作物病害智能识别的推广具有较好的借鉴意义。
위료준학식별소맥협부상견병해，위소맥병정진단화발전상황판단제공과학의거，설계병실현료일충기우SVM （ Support Vector Machine ）적소맥협부상견병해식별방법。해방법가이실현대소맥백분병、조수병화협수병적준학식별。수선，기우중치려파법화K균치취류산법，실현료도상적거조급병반분할；연후，제취료병반구역형상특정화문리특정；최후，이용SVM 산법대소맥협부병해진행료분류식별。수궤시험결과표명，이용소제취적특정가이유효지실현소맥협부상견병해적식별，기우형상특정적종합식별솔가체99．33％；이용 SVM 산법진행소맥병해협편식별시유효적、가행적。해방법대우농작물병해지능식별적추엄구유교호적차감의의。
In order to identify wheat blade diseases accurately and provide scientific basis for wheat diseases diagnosis , an identification method of wheat blade disease based on SVM was presented .Three wheat blade diseases , such as Pow-dery mildew, Strip rust disease and Blade rust disease , were recognized accurately via this method .First, image de-noi-sing and spot segmentation were achieved by using median filter method and K-means clustering algorithm .Then , two kinds of disease spot features were extracted , including shape features and texture features .Finally , disease blades were classified and identified using SVM algorithm .Experiment results showed that the presented method which took advantage of the disease spot features could identify wheat blade diseases efficiently , and the comprehensive identification rate of shape features based method reached 99 .33%, which showed that it was very effective and practicable for identifying wheat blade diseases by using SVM algorithm .The presented method also provides reference for intelligent identification of crop diseases in the future research .