Work

Studying honeybees' landing on flower: an experimental setup

Year: 2025

Type: article

Abstract: Landing a helicopter on a moving ship is an error-prone operation, requiring the expertise of highly trained pilots, and benefits greatly from advanced technologies to improve decision-making and action regulation. (Thomas et al. 2021). In stark contrast, landing to forage on a flower looks to be performed with remarkable ease and under visual control in bees (Lehrer and Srinivasan 1992; de Vries et al 2025). Understanding the perceptual behavior of pilots in comparison to that of honeybees is a key driver of innovation in the design of bio-inspired robotic systems. Understanding the mechanisms behind this ability requires accurately tracking the bee’s movement during the landing task. Vision-based tracking techniques using convolutional neural networks (CNNs), such as You Only Look Once (YOLO) algorithm (Redmon et al. 2016), can be specifically trained to detect and track the honeybee-flower system. Our goal is to accurately reconstruct a known trajectory, using a fake bee as a target, with a mean tracking error less than half a centimeter, or about half the length of a honeybee (13 mm).

Source: Multidisciplinary Biomechanics Journal

DOI: 10.46298/mbj.16142

Authors: Nicolas Salvage, Antoine H. P. Morice, Julien R. Serres

Institutions: Aix-Marseille Université, Institut des Sciences du Mouvement Etienne-Jules Marey

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4415308480

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