为进一步提高不同品质小麦分类模型的检测精度, 提出采用太赫兹时域光谱技术(THz-TDS), 融合小麦样品的吸收光谱和折射率光谱信息, 对其品质进行检测识别。 以正常小麦、 发芽小麦、 霉变小麦和虫蚀小麦样品为研究对象, 获取样品THz波段光学参数, 在特征层选用AdaBoost(AdaBoost)分类器和支持向量机(SVM)方法, 建立了小麦品质多项光学指标的分类融合模型。 并将融合模型的识别结果进行比较, 结果表明融合模型对小麦样品的识别率达到95%。 最后, 为了验证融合模型的有效性, 将其与单光谱分析回归模型进行了对比, 表明融合模型比单光谱模型在小麦样品的识别率上有了较大的提高, 且SVM融合模型的识别率最高, 是一种最优的多源信息融合方法。

In order to improve the measurement precision of identification models, multisource information fusion technique was employed to identify four types of wheat grain (normal, worm-eaten, moldy, and sprouting wheat grains). The terahertz (THz) spectra of wheat grains with various degrees of deterioration were investigated; classification fusion models were constructed by using THz absorption spectra combined with refractive index spectra. The adaboost and SVM were used in the feature level fusion models. The results showed that the different wheat samples were identified with an accuracy of nearly 95%. Furthermore, fusion models results of wheat detection were compared with results from other methods, the comparisons showed that the recognition ratio of fusion models had a great improvement, and the SVM model outperformed the others.