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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

石晶晶、刘占宇、张莉丽、周湾、Jingfeng Huang

2009Chemistry被引 1

摘要

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.

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