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A tool for developing an automatic insect identification system based on wing outlines.

Yang HP, Ma CS, Wen H, Zhan QB, Wang XL - Sci Rep (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Entomology, China Agricultural University, Beijing, China.

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.

No MeSH data available.


Related in: MedlinePlus

The results of the principal component analysis illustrate the landscape of misidentification between all 7 owlflies species using wing outlines.PC1 and PC2 are the first and second principal components, respectively.
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f3: The results of the principal component analysis illustrate the landscape of misidentification between all 7 owlflies species using wing outlines.PC1 and PC2 are the first and second principal components, respectively.

Mentions: The detailed misidentification rates for seven species of Ascalaphidae are shown in Table 3. P. elwesii had the greatest likelihood (8.33%) of being confused with Maezous umbrosus. Ascalaphus placidus was also relatively easily confused with L. sibiricus (misidentification rate = 6.67%). To illustrate the landscape of misidentification among all 7 species in this study, we conducted a principal component analysis (PCA) and used the first two principal components to construct a scattered graph (Fig. 3). The wing outlines are overlapped among 4 species, i.e., Acheron trux, M. umbrosus, P. elwesii, and A. placidus, but are clearly separated from those of A. subjacens and P. japonicus. Thus, improving the identification accuracy of these three owlflies species is difficult when using only wing outlines.


A tool for developing an automatic insect identification system based on wing outlines.

Yang HP, Ma CS, Wen H, Zhan QB, Wang XL - Sci Rep (2015)

The results of the principal component analysis illustrate the landscape of misidentification between all 7 owlflies species using wing outlines.PC1 and PC2 are the first and second principal components, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528224&req=5

f3: The results of the principal component analysis illustrate the landscape of misidentification between all 7 owlflies species using wing outlines.PC1 and PC2 are the first and second principal components, respectively.
Mentions: The detailed misidentification rates for seven species of Ascalaphidae are shown in Table 3. P. elwesii had the greatest likelihood (8.33%) of being confused with Maezous umbrosus. Ascalaphus placidus was also relatively easily confused with L. sibiricus (misidentification rate = 6.67%). To illustrate the landscape of misidentification among all 7 species in this study, we conducted a principal component analysis (PCA) and used the first two principal components to construct a scattered graph (Fig. 3). The wing outlines are overlapped among 4 species, i.e., Acheron trux, M. umbrosus, P. elwesii, and A. placidus, but are clearly separated from those of A. subjacens and P. japonicus. Thus, improving the identification accuracy of these three owlflies species is difficult when using only wing outlines.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Entomology, China Agricultural University, Beijing, China.

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.

No MeSH data available.


Related in: MedlinePlus