DNA Barcoding:An Effective Tool for Species Identification of Lepidopteran Oak Pests

[Objective] The paper was to improve the efficiency and accuracy of early forecast of Lepidopteran oak-infesting pests.[Method] DNA barcoding technique was established for quick species identification using mitochondrial cytochrome C oxidase subunit Ⅰ（COⅠ） as the standard gene.This barcoding technique was used to amplify and sequence genomic DNA samples from eggs and pupae of 11 species of Lepidopteran pests collected from oak.[Result] The DNA barcoding standard genes of 594-708 bp were determined from eggs and pupae of Lepidopteran insects.There were differences of 0-2 bases in DNA barcode sequences between conspecific eggs and pupae,with the sequence identity of 99.7%-100%.The average content of A,T,G and C of DNA barcode sequences from Lepidopteran insects were 30.7%,38.5%,14.9% and 15.9%,respectively.The obtained DNA barcode sequences had 91.4%-100% identity and 0-8.6% difference degree with GenBank-deposited DNA barcode sequences from organisms of the genetically-closest relationship.Among them,DNA barcode sequences from egg and pupa samples of 10 Lepidopteran insects（No.1-20） had 99%-100% identity and 0-1.0% difference degree with homologous sequences in GenBank database,while the remaining samples（No.21-22） had high difference degree（8.6%） with homologous sequences.[Conclusion] The established DNA barcoding technique is an effeetive tool for species identification of Lepidopteran pests using genomic DNA from eggs and pupae of Lepidopteran insects.

Insect recognition based on integrated region matching and dual tree complex wavelet transform

To provide pest technicians with a convenient way to recognize insects,a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT).The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated.The ROI is first segmented with the k-means algorithm into regions according to the color features,properties of all the segmented regions being used as a coarse level feature.The color image is then converted to a grayscale image,where DTCWT features are extracted as a fine level feature.The IRM scheme is undertaken to find K nearest neighbors (KNNs),out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features.The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%.For the forewing subset,a recognition accuracy of 92.38% was achieved.The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.