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# Studying honeybees' landing on flower: an experimental setup

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

► To cite this version:

Nicolas Salvage, Antoine H. P. Morice, Julien R. Serres. Studying honeybees' landing on flower: an experimental setup. 50e congrès de la Société de Biomécanique, Oct 2025, Marseille, France., 2025. hal-05344630

HAL Id: hal-05344630

https://hal.science/hal-05344630v1

Submitted on 3 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Studying honeybees' landing on flower: an experimental setup

Nicolas Salvage¹, Julien R Serres¹, Antoine HP Morice¹

¹Aix-Marseille université, CNRS, Institut des Sciences du Mouvements (ISM), Marseille, France

Corresponding author : nicolas.salvage@univ-amu.fr

## Introduction

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* 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 [1]

In this study, we thus aim to investigate how honeybees land to compare it with the literature on pilots [1].

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

* Landing to forage on a flower looks to be performed with remarkable ease and under visual control in bees [2,3,4]

## Results

<figure><img src="image_6.png" /><figcaption>Left view</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Right view</figcaption></figure>

* Pearson correlation :
  $r_X = 0.9998, r_Y = 0.9998, \text{and } r_Z = 0.9998; \text{ all } p < 0.001, n = 150$

* Mean error & RMSE :
  $x = -5,4 \text{ mm}; y = 3,7 \text{ mm}; z = 12,3 \text{ mm}$
  $\text{RMSE}_X = 14 \text{ mm}; \text{RMSE}_Y = 6,3 \text{ mm}; \text{RMSE}_Z = 14,3 \text{ mm}$

## Conclusion

* ✓ Accurate visual tracking obtained for a linear movement

* ✓ Operational method for behavior analysis by computer vision

* ✓ Prospects: extension to the natural environment

## Methodology

Our goal is to accurately reconstruct a known trajectory, using a fake bee as a target, with a mean tracking error of less than half a centimeter

* Vision-based tracking techniques using CNNs, such as You Only Look Once (YOLO) algorithm [5]

<figure><img src="image_8.png" /><figcaption>Top view of flight arena</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Front View of flight arena</figcaption></figure>

* YOLO Training : ADAM optimizer : 30 epochs & 300 iterations; imgz = 640; Dataset = 372 images

<figure><img src="image_10.png" /><figcaption>Trajectory reconstruction process with</figcaption></figure>

Matlab® and YOLO

<footnote>[1] Thomas & al., Scientific Reports (2023).</footnote>

<footnote>[2] Zhang & al., Vis Neurosci (1990).</footnote>

<footnote>[3] Lehrer & Srinivasan, Journal of Comparative Physiology (1992).</footnote>

<footnote>[4] de Vries & al., Journal of Experimental Biology (2025).</footnote>

<footnote>[5] Redmon & al., arXiv (2016).</footnote>

<footnote>[6] The MathWorks, Inc., Computer Vision Toolbox (2025).</footnote>