Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics

Tanvir Bhuiyan; Ryan M Carney; Sriram Chellappan

doi:10.1016/j.isci.2022.104924

Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics

iScience. 2022 Aug 13;25(9):104924. doi: 10.1016/j.isci.2022.104924. eCollection 2022 Sep 16.

Authors

Tanvir Bhuiyan¹, Ryan M Carney², Sriram Chellappan¹

Affiliations

¹ Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.
² Integrative Biology, University of South Florida, Tampa, FL 33620, USA.

Abstract

Many groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms-specifically, computer vision-to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of $91.71 %$ and $88.86 %$ , respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.

Keywords: Artificial intelligence; Bioinformatics; Computing methodology; Entomology; Zoology.