Identification of tobacco components in cut filler based on convolutional neural network

For evaluating the blending consistence in cigarette production, the make-up of cut filler, including cut strips, cut stems, expanded cut strips and cut reconstituted tobacco, was identified with a model based on convolutional neural network incorporating deep learning approach. The local images, which reflected the microstructural characteristics of cut filler were used as the inputs of neural network. The output corresponding to each local characteristic image was analyzed and identified, and via statistical analysis the constituents of cut filler were determined. The results showed that the identification accuracies of the model for training samples and testing samples were 100% and 84.95%, respectively. The convolutional neural network combining with the corresponding method of result expressing in the model effectively promotes the identification accuracy for cut filler samples.