Unmanned Aerial Vehicle-based Autonomous Tracking System for Invasive Flying Insects

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Declining honeybee populations can severely affect agricultural productivity and biodiversity because honeybees provide a crucial ecological service, that is, pollination. The Asian hornet or yellow-legged hornet, Vespa velutina nigrithorax , is a global predator of honeybees ( Apis mellifera L. ) that has become widespread owing to rapid climate change. Given the ecological effect of this invasive alien species, effective management strategies are needed. Herein, we propose a localization system for tracking radio-tagged hornets and discovering hornet hives by combining unmanned aerial vehicles (UAVs) with a trilateration system. By leveraging the homing instinct of hornets, we systematically structured our experiments into a behavioral experiment, ground-truth experiment, and localization experiment. We installed two antennas around apiary premises and an additional antenna mounted on a UAV to track hornets equipped with sensors. According to the experimental results, we successfully discovered the hives of two of the five hornets tested. The average localization error for the first hive was latitude 0.0006(±0.0002) and longitude 0.0023(±0.0015) in decimal degrees. The average localization error for the second hive was latitude 0.0012(±0.0001) and longitude 0.0004(±0.0011) in decimal degrees. Additionally, we thoroughly analyzed the experimental results to obtain insights into hornet behavior and movement patterns. We explored the practicality and scalability of sensor-based tracking methods utilizing UAVs to ascertain their potential for widespread adoption and future advancement. Our study underscores the importance of using innovative technologies to address the ecological challenges posed by invasive species for facilitating ecosystem conservation and management efforts.

Article activity feed