Recognition Among Three Kinds of Pteridophyte Plants Based on Fourier Transform Infrared-Discrete Wavelet Feature Extraction and Artificial Neural Network Classification Method

Abstract

Fourier transform infrared(FT-IR) and horizontal attenuated total reflectance(HATR) techniques were used to obtain the FT-IR of three kinds of pteridophyte plants(the root of Cyrtomium fortunei J.Sm,Dryopteris championii(Bench) C.Chr.apud Ching and Dryopteris varia(L.) O.Ktze.).The similar features of FT-IR among the root of Cyrtomium fortunei J.Sm,Dryopteris championii(Bench) C.Chr.apud Ching and Dryopteris varia(L.) O.Ktze.were extracted by discrete wavelet transform.The scale 4 and 5 were used to extract the feature vectors,which were used to train the artificial neural network(ANN).The trained neural network was used to classify the root of Cyrtomium fortunei J.Sm,Dryopteris championii(Bench) C.Chr.apud Ching and Dryopteris varia(L.) O.Ktze.,which were collected from different places.According to 240 prediction samples,we could effectively identify the root of Cyrtomium fortunei J.Sm,Dryopteris championii(Bench) C.Chr.apud Ching and Dryopteris varia(L.) O.Ktze.by FT-IR with discrete wavelet feature extraction and artificial neural network classification.