基于支持向量机（SVM）的稻纵卷叶螟危害水稻高光谱遥感识别

 对健康水稻叶片以及受稻纵卷叶螟危害后的水稻叶片进行了室内光谱的测定及分析。对430~530 nm和560~730 nm波段采用连续统去除的方法，分别提取了波深、斜率参量作为径向基核函数支持向量机的输入变量，利用LIBSVM软件包构建叶片高光谱识别模型。当参数γ和惩罚系数C分别取0.25和1时构建的径向基支持向量机模型的分类性能最佳，识别精度达100%。研究结果为实时水稻病虫害的早期监测以及田间管理提供了一定的理论基础。

Hyperspectral Recognition of Rice Damaged by Rice Leaf Roller Based on Support Vector Machine

The spectra of healthy leaves and leaves damaged by the rice leaf roller were measured and analyzed by the method of continuum removal. In the range of 430-530 nm and 560-730 nm, the band depth and slope were extracted. Then the extracted parameters were chosen as the input vector of the support vector machine (SVM) to design a support vector classifier for the recognition of the leaves damaged by the rice leaf roller. The results confirmed that the classification precision of the SVM with radial basis function(RBF) kernel function was as high as 100% when γ and C were 0.25 and 1, respectively. This could provide theoretic basis for farmers to recognize the rice leaf damaged by the rice leaf roller on time and control it effectively.