Dec 18, 2015

Insect Wing Classification of Mosquitoes and Bees Using CO1 Image Recognition.

BioRxiv : the Preprint Server for Biology
Nayna Vyas-PatelJohn D Mumford

Abstract

The certainty that a species is accurately identified is the cornerstone of appearance based classification; however the methods used in classical taxonomy have yet to fully catch up with the digital age. Recognising this, the CO1 algorithm presented on the StripeSpotter platform was used to identify different species and sexes of mosquito wings (Diptera: Culicidae) and honey bee and bumblebee wings (Hymenoptera: Apidae). Images of different species of mosquito and bee wings were uploaded onto the CO1 database and test wing images were analysed to determine if this resulted in the correct species being identified. Out of a database containing 925 mosquito and bee wing images, the CO1 algorithm correctly identified species and sexes of test wing image presented, with a high degree of accuracy (80% to 100%) depending on the species and database used, excluding sibling species) highlighting the usefulness of CO1 in identifying medically important as well as beneficial insect species. Using a larger database of wing images resulted in significantly higher numbers of test images being correctly identified than using a smaller database. The hind wings of Hymenoptera provided higher levels of correctly identified results than using th...Continue Reading

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Computer Software
Apis mellifera
Classification
Apis mellifera preparation
Hymenoptera
Genus Apis (organism)
Apidae
Genus Brachygobius
Diptera
Recognition (Psychology)

About this Paper

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.