Tobacco Leaf Maturity Classification Based on Sparse Auto-encoder

For reducing labor cost, a leaf maturity classifying algorithm based on sparse auto-encoder was proposed, wherein leaf maturity was automatically identified by computer vision. The digital image of tobacco leaf was pretreated via background removing and normalizing at first; next, a feature learning network was established by means of a sparse auto-encoder on the basis of unsupervised learning algorithm; then, the features were expanded via a partially connected network as a solution to tobacco leaf image processing; finally, Softemax regression was used to classify the learned features. The proposed algorithm extracted the required features from pixel level directly without purposely selecting design features. The results of simulation showed that when the sparse auto-encoder was applied to tobacco leaf maturity classification, its accuracy of data classification reached 98.63%, which was higher than that of other classifiers.