Abstract

It is well acknowledged that flue-tobacco aroma types were divided into light, medium and heavy in China. For the sake of singling out an optimal scheme to discriminate the spatial distribution of flue-cured tobacco aroma type, in the current study, different amounts of chemical indices data with various methods including Back-Propagation Neural Networks (BP NN), Support Vector Machine (SVM) and Discriminant Analysis (DA) were presented and compared. All the experimental results indicated that, by and large, the number of chemical indices have nothing to do with the accuracy. Additionally, the classification effects of BP NN are superior to the others. On a whole, the best scheme with accuracy reaching to 81.18% and kappa value up to 0.72 was drawn only when the BP model combined with 9 kinds of chemical indices. In the end, the optimal spatial distribution was established in ArcGIS9.3.

Keywords:

BP NN, DA, flue-cured tobacco aroma, spatial classification, SVM,

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