title: A tool for developing an automatic insect identification system based on wing outlines.
abstract: For some insect groups, wing outline is an important character for species identification. We have constructed a program as the integral part of an automated system to identify insects based on wing outlines (DAIIS). This program includes two main functions: (1) outline digitization and Elliptic Fourier transformation and (2) classifier model training by pattern recognition of support vector machines and model validation. To demonstrate the utility of this program, a sample of 120 owlflies (Neuroptera: Ascalaphidae) was split into training and validation sets. After training, the sample was sorted into seven species using this tool. In five repeated experiments, the mean accuracy for identification of each species ranged from 90% to 98%. The accuracy increased to 99% when the samples were first divided into two groups based on features of their compound eyes. DAIIS can therefore be a useful tool for developing a system of automated insect identification.
authors.affiliation: Department of Entomology, China Agricultural University, Beijing, China.
authors.forename: He-Ping
authors.initials: HP
authors.lastname: Yang
authors.affiliation: Climate Change Biology Research Group, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.
authors.forename: Chun-Sen
authors.initials: CS
authors.lastname: Ma
authors.affiliation: Department of Entomology, China Agricultural University, Beijing, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Wen
authors.affiliation: Nanjing Forest Police College, Nanjing, China.
authors.forename: Qing-Bin
authors.initials: QB
authors.lastname: Zhan
authors.affiliation: Department of Entomology, China Agricultural University, Beijing, China.
authors.forename: Xin-Li
authors.initials: XL
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007313:Insecta / Q000033:anatomy & histology* / Q000145:classification; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D013045:Species Specificity; D060388:Support Vector Machine; D014921:Wings, Animal / Q000033:anatomy & histology*