基于多光谱成像技术的小麦品种快速无损鉴定

Rapid and Nondestructive Identification of Wheat Varieties with Multispectral Imaging Technology

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。

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.