A method for identifying types of tobacco strands based on residual neural network

For rapidly and accurately identifying expanded tobacco and the strands of tobacco strips, stems, expanded tobacco and reconstituted tobacco, an identification model on the basis of residual neural network was developed by their image characteristics. The pre-training weights, optimized algorithm, learning rate and other super parameters of the model were studied as well. The results indicated that: 1) The developed method could effectively identify the above mentioned four types of tobacco strands, and the developed model presented higher identification rate, generalization capability and robustness comparing with the model based on convolutional neural network. 2) The super parameters influenced the training speed and performance of the model obviously, and the identification rate and recall rate of the model trained with the optimized super parameters were higher than 96% for the test set, which were close to that of the training set. This method provides a support for promoting the efficiency and accuracy of tobacco strands identification.