中文摘要：

小麦条锈病是我国小麦生产上造成损失最大、危及范围最广的一种病害,对该病的监测预报是实施有效治理措施的重要基础和依据。文章以88个小麦叶片为试验材料,其中条锈病叶按严重度分为8级,健康小麦叶片为对照,由ASD Field-Spec Pro FR 2500型光谱仪和LI-Cor1800-12外置积分球获取高光谱数据,采用SVM算法对不同严重度的小麦条锈病病叶进行了判别分析。按1∶1比例随机划分样品集,校正集的44个样品建立模型,对预测集的44个样品的严重度进行预测识别,总体正确识别率达97%,表明SVM算法用于小麦条锈病严重度分级识别是可行的。

英文摘要：

Wheat stripe rust,caused by Puccinia striiformis f.sp.tritici,is one of pandemic diseases causing severe losses in China.Monitoring and warning of this disease is principal for its precise prediction and for implementing effective measures to control it.The hyperspectral data used for analysis were attained from 88 leaves including healthy leaves and infected leaves over a range of disease severity levels.Support vector machine（SVM） was applied to classify and identify the severity of wheat leaves infected by the pathogen.The model was built based on 44 proof-read samples to estimate 44 proof-test samples.And the identification accuracy is totally 97%.So SVM can be used in the classification and identification of severity of wheat stripe rust based on attained hyperspectral data.