Identification Method of Green Tobacco Leaf Positions Based on Hyperspectral Imaging Technology
s:In order to realize the rapid non-destructive identification of fresh tobacco leaf position,this study took tobacco leaves with different leaf positions as the research object,applied hyperspectral imaging technology to construct a fresh tobacco leaf position discrimination model based on characteristic spectrum.We processed the original hyperspectral data of tobacco leaves by using SNV(standard normal variate),2ND(2nd derivative),SG(Savitzky-Golay smoothing filter)and MSC(multiplicative scatter correction)four spectral preprocessing methods,and used the preprocessed full-band spectral data and characteristic band spectral data to construct fresh tobacco leaf position recognition models based on SVM(support vector machines),PLS-DA(partial least squares-discriminant analysis,PLS-DA)and BPNN(back propagation neural network).The results showed that the model constructed by SG filter preprocessing and BPNN had the best recognition effect,and the discrimination results of the training set and prediction set were 91.15%and 90.63%,respectively.In addition,the BPNN model established by using the characteristic wavelengths screened by CARS was the best,and the prediction accuracy of the training set and prediction set reached 93.23%and 92.19%.This study shows that it is feasible to use hyperspectral imaging technology to identify the parts of fresh tobacco leaves,which can realize rapid and nondestructive detection of the parts of fresh tobacco leaves.
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