MonarchNet: Differentiating Monarch Butterflies from Butterflies Species with Similar Phenotypes

TL;DR

MonarchNet is a deep learning-based system that accurately differentiates monarch butterflies from similar-looking species, aiding in ecological research and conservation efforts.

Contribution

This paper introduces MonarchNet, the first comprehensive butterfly dataset and a baseline deep learning model for distinguishing monarchs from look-alike species.

Findings

High classification accuracy achieved

Effective differentiation of monarchs from look-alikes

Potential to improve butterfly population monitoring

Abstract

In recent years, the monarch butterfly's iconic migration patterns have come under threat from a number of factors, from climate change to pesticide use. To track trends in their populations, scientists as well as citizen scientists must identify individuals accurately. This is uniquely key for the study of monarch butterflies because there exist other species of butterfly, such as viceroy butterflies, that are "look-alikes" (coined by the Convention on International Trade in Endangered Species of Wild Fauna and Flora), having similar phenotypes. To tackle this problem and to aid in more efficient identification, we present MonarchNet, the first comprehensive dataset consisting of butterfly imagery for monarchs and five look-alike species. We train a baseline deep-learning classification model to serve as a tool for differentiating monarch butterflies and its various look-alikes. We seek to contribute to the study of biodiversity and butterfly ecology by providing a novel method for computational classification of these particular butterfly species. The ultimate aim is to help scientists track monarch butterfly population and migration trends in the most precise and efficient manner possible.