Study of near infrared spectrum classification for tobacco leaf position based on deep belief network

As a fast and nondestructive detection method, near infrared detection has been widely concerned. But a lot of noises, high dimension and nonlinearity of the spectra affect the accuracy of classification model. In this study, deep belief network (DBN) theory was improved and introduced into spectral features learning to solve the difficulty of learning nonlinear relation of high dimensional data. The strategy based on layer by layer and stochastic gradient ascent algorithm were used for acquiring the network weights. Combined with the SVM method, the DBN-SVM multi classification model of tobacco leaf position was established. The proposed method, was compared with PCA-SVM method based on principal component analysis and LDA-SVM method based on linear discriminant analysis.The results show that DBN-SVM method may effectively learn the internal structure and nonlinear relationship of high dimensional data. The model constructed by this algorithm not only has excellent performance in identification and feature learning, but also is superior in robust, sensitivity and specificity.