马铃薯品质鉴别的近红外光谱与多源信息耦合

建立了基于机器视觉和近红外光谱技术的分级概率输出，利用DS证椐融合规则，搭建适用于异源数据的无损检测分级决策模型。采用方向梯度直方图和主成分提取方法提取光谱特征，并应用支持向量机和AdaBoost分类器进行识别，在此基础上，构建了基于特征层融合的马铃薯分级模型。采用多源信息融合技术，建立了融合无损检测分级决策和特征层融合的多源信息融合农产品品质鉴别模型。仿真结果表明，相比于单一鉴别模型，多源信息融合鉴别模型识别率提高了12.7%~30.2%，达95.7% 以上。

Coupling method of near infrared spectroscopy and multi source information for potato quality identification

The classification probability output based on machine vision and near infrared spectroscopy technology was established, and the classification decision model suitable for heterogeneous data was built by using DS evidence fusion rules. The directional gradient histogram and principal component extraction method were used to extract spectral features, and support vector machine and AdaBoost classifier were used for recognition. On this basis, the potato classification model based on feature layer fusion was constructed. By using multi-source information fusion technology, a multi-source information fusion agricultural product quality identification model was established, which integrated the classification decision of nondestructive testing and feature level fusion. The simulation results showed that, compared with the single identification model, the recognition rate of multi-source information fusion identification model was improved by 12.7%~30.2%, and reached more over 95.7%.