# 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. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the., 10.46298/mbj.16142. hal-05191694v2

HAL Id: hal-05191694

https://hal.science/hal-05191694v2

Submitted on 23 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

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

Nicolas Salvage<sup>a*</sup>, Antoine H. P. Morice<sup>a</sup>, Julien R. Serres<sup>a, b</sup>

<sup>a</sup> Aix Marseille Univ, CNRS, ISM, Marseille, France

<sup>b</sup> Institut Universitaire de France, IUF, Paris, France

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

Received date: 07/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** stereovision, biolocomotion, insect behavior, motion capture, affordance, 3D flight

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

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 system. 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 5 mm, about half the size of a Bee.

## 2. Methods

### 2.1 Flight arena and imaging setup

The flight was observed within a 55 cm long × 45 cm width × 46 cm height flight arena, surrounded by plexiglass walls. An artificial flower, robotically controlled, moved to the bottom of the flight arena. Two Sony RX0 cameras (1280 × 720 pixels, @120 fps) were placed at

a distance of 1.35 meters from the flight arena and positioned at an angular offset of 15° relative to each other. The cameras were temporally synchronized using two Sony CCB-WD1 control boxes to ensure frame-level precision.

### 2.2 Experimental setup

Reliable ground-truth trajectory was pursued using a 13mm 3D-printed bee, moving along a taut fishing line. The 3D-printed bee was moving on a known linear path (from corner 8 to point 2 in fig. 1A) to ensure consistent motion across all spatial dimensions. A more complex trajectory would not add to the reconstruction error, because the error is in the triangulation, not in its complexity.

### 2.3 Camera calibration

Dynamic calibration was expected to correct geometric distortions induced by the plexiglass walls and align the epipolar geometry of the stereo video system. While running MATLAB® Computer Vision Toolbox™, a 6 × 9 checkerboard pattern with 2.9 mm-width squares was manually oriented within the flight arena. This procedure returned an estimation of intrinsic and extrinsic camera parameters as well as lens distortion coefficients. The coordinates of colored markers placed at the 8 corners of the arena were compared to reprojected points generated by the calibration model. The average reprojection error was equal to 0.15 pixel.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** (A) Left and (B) right views of the stereovision system with corner detection (green ○), 3D printed bee (red ○) and frame of reference (RGB for x, y and z) (C) Front and (D) top views of reconstructed 3D printed motion (theoretical = blue ○, measured = red ★).</figcaption></figure>

Credit: Nicolas Salvage.

## 2.4 Training and inference

A dataset comprising 193 annotated frames was collected from various camera perspectives to train the YOLO network, using the ADAM optimizer over 30 epochs and 300 iterations. Prior to inference, the input video sequences underwent pre-processing in MATLAB to reduce background noise. Subsequently, the trained YOLO model was deployed to perform object detection and tracking of the fake bee with the *bytetrack* algorithm on 6 trajectories.

## 3. Results and discussion

The comparison between the YOLO-based 3D reconstruction and the 3D printed bee trajectory resulted in a high Pearson correlation coefficient for all axes ($r_x = 0.9998$, $r_y = 0.9998$, and $r_z = 0.9998$; all $p < 0.001$, $n = 150$). This suggests a nearly perfect linear correspondence between the measured and reference trajectories. Despite these high correlations, the error metrics varied among the axes. The mean error ranged from -5mm along the X-axis to 12mm along the Z-axis. Moreover, the Root Mean Squared Error (RMSE) values for the

X and Z axes were comparable, suggesting a similar overall error magnitude in these dimensions. Notably, the discrepancy between the initial and final data points was more pronounced at the beginning of the trajectory, implying that measurement accuracy was particularly sensitive when the target is farther from the cameras.

<table><caption>**Table 1.** Differences (in mm) between YOLO-based 3D reconstruction and 3D-printed bee path.</caption><thead><tr><th></th><th>mean error</th><th>Standard deviation</th><th>RMSE</th><th>Delta first & last point</th></tr></thead><tbody><tr><td><b>x</b></td><td>-5.4</td><td>8.8</td><td>14.0</td><td>-20.3; 11.0</td></tr><tr><td><b>y</b></td><td>3.7</td><td>3.3</td><td>6.3</td><td>-1.5; -2.7</td></tr><tr><td><b>z</b></td><td>12.3</td><td>4.8</td><td>14.3</td><td>20.8; 3.2</td></tr></tbody></table>

Overall, we find an error close to half a centimeter, which is more accurate than current systems (Zhao et al. 2025). The observed differences in the order of half a centimeter between the measured and theoretical
trajectories can be attributed to several factors. The most significant error occurs at the beginning of the trajectory, where the fake bee is located at a greater distance from the cameras. At such distances, a single pixel represents a larger physical displacement, thereby magnifying any positional error. Our tracking approach relies on using the center of the YOLO detection bounding box to represent the object's position. However, slight deviations in the detected center from frame to frame likely due to limitations in the current training of the model result in variability that is negligible when the target is close, but becomes significant when the object is farther away. Additionally, tracking is sometimes lost, which further contributes to the discrepancies observed between the measured and theoretical trajectories.

## 4. Conclusions

While the system's performance has not been optimal yet, these findings underscore the feasibility of the experimental setup for high-frequency (120 fps) 3D tracking of a free-flying insect with an average error close to half a centimeter. This work lays the foundation for further improvements in vision-based tracking of insects, ultimately contributing to a better understanding how flying insects perform a landing task on a moving flower, which remains an equivalent complex for aerial robots landing on a ship deck. Future improvements will focus on enhancing the robustness of the YOLO model through more extensive training and refining the detection process, especially for objects at longer distances from the camera.

## Conflict of Interest Statement

None.

## Funding

This research was supported by the French Agence de l'innovation de défense (AID) (Grant DGA-2024-65-0013), the CNRS and Aix Marseille University (amU).

## Contributor Roles

NS: Conceptualization, Investigation, Methodology, Resources, Writing original draft, Methodology, Investigation, Software, Data curation. JRS and AHPM: Conceptualization, Supervision, Validation.

## Data, software, code availability

https://github.com/Nico2366/BeeTracking

## References

de Vries, L. J., van Langevelde, F., van Leeuwen, J. L., Naguib, M., Pieters, R. P., & Muijres, F. T. (2025). Follow the flower: approach-flight behaviour of bumblebees landing on a moving target. *J.Exp.Bio.*, 228(6). https://doi.org/10.1242/jeb.249380

Lehrer, M., & Srinivasan, M. V. (1992). Freely flying bees discriminate between stationary and moving objects: performance and possible mechanisms. *J. Comp. Physiol. A*, **171**, 457–467. https://doi.org/10.1007/BF00194578

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In *Proceedings of the IEEE conference on computer vision and pattern recognition* (pp. 779-788). Las Vegas, NV, USA. https://doi.org/10.1109/CVPR.2016.91

Thomas, M., Pereira Figueira, J. M., Serres, J. R., Rakotomamonjy, T., Ruffier, F., & Morice, A. H. (2021). Helicopter pilots synchronize their altitude with ship heave to minimize energy when landing on a ship's deck. *Int. J. Aerosp. Psychol.*, **31**(2), 135–148. https://doi.org/10.1080/24721840.2020.1862659

Zhao, N., Lifeng W., & Ke W. (2025). Insights into mosquito behavior: Employing visual technology to analyze flight trajectories and patterns. *Electronics* 14(7), 1333. https://doi.org/10.3390/electronics14071333