Migrating Monarch Butterfly Localization Using Multi-Sensor Fusion Neural Networks
This addresses the challenge of studying elusive butterfly migration patterns for ecological researchers, representing a domain-specific incremental advance.
The paper tackles the problem of tracking Monarch butterfly migration by developing a deep learning algorithm that estimates daily locations from light and temperature sensor data, achieving mean absolute errors of <1.5° in latitude and <0.5° in longitude.
Details of Monarch butterfly migration from the U.S. to Mexico remain a mystery due to lack of a proper localization technology to accurately localize and track butterfly migration. In this paper, we propose a deep learning based butterfly localization algorithm that can estimate a butterfly's daily location by analyzing a light and temperature sensor data log continuously obtained from an ultra-low power, mm-scale sensor attached to the butterfly. To train and test the proposed neural network based multi-sensor fusion localization algorithm, we collected over 1500 days of real world sensor measurement data with 82 volunteers all over the U.S. The proposed algorithm exhibits a mean absolute error of <1.5 degree in latitude and <0.5 degree in longitude Earth coordinate, satisfying our target goal for the Monarch butterfly migration study.