【摘要】 为了研究多光谱成像技术对小麦品种快速无损鉴定的可行性,采用VideometerLab多光谱图像采集设备对5个小麦品种共500个样品在405～970 nm波段内的进行多光谱图像信息进行采集,获取其光谱、颜色和形态特征。利用主成分分析对5个小麦品种进行定性鉴别,同时,基于光谱特征和光谱图像特征分别比较了神经网络、支持向量机和随机森林3种模型的鉴定效果。结果显示:利用19个光谱特征值建立的模型中,BPNN识别模型效果最佳,其建模集和预测集的识别率分别为100%和91.25%。融合19个光谱特征和6个图像特征所建立的模型中,BPNN识别模型效果最佳,其建模集和预测集的识别率分别达到了100%和98.4%。结果表明,基于BPNN的多光谱特征融合能够有效的提高小麦品种鉴定效率,为小麦品种的快速无损检测提供了一个新途径。更多还原

【Abstract】 To study the feasibility of multi-spectral imaging technology for rapid and non-destructive identification of wheat varieties, multi-spectral images that covered the range of 405-970 nm from 500 samples of 5 wheat varieties were collected to find out the specified spectral, color and morphological characteristics by using VideometerLab multiidentified by principal component analysis. Likewise, the recognition accuracy of 3 different models(neural network, support vector machine and random forest) was compared based on spectral features and spectral image features. The results showed that BPNN method had the best performance, which was 100% and 91.25%for the modeling set and prediction set respectively, when 19 spectral eigenvalues were employed. Moreover,the satisfactory recognition accuracy, which was 100% and 98.4% for the modeling set and prediction set respectively, was also achieved when 19 spectral features and 6 image features were integrated. It suggested that multi-spectral feature fusion based on BPNN can effectively improve the recognition accuracy of wheat varieties and provide a new way for rapid nondestructive detection of wheat varieties.更多还原