Insect Wing Classification of Mosquitoes and Bees Using CO1 Image Recognition

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 the fore wings. The software should be used in conjunction with other identifying criteria (salient morphological features) in addition to the wings. CO1 is a powerful algorithm to use in identifying insect wings in its current form and has great potential if it is adapted and tailored for insect species identification. It is suggested that a primary aim in the digital age should be the production of a ‘World Wide Database’ of insect images, where all known insect images can be made available to everyone, with image recognition and species knowledge at its core.