[Dataset] Non-invasive Honeybee Colony Monitoring via Robotic Mapping of Combs in Observation Hives
Authors/Creators
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Janota, Jiří
(Researcher)1
-
Blaha, Jan
(Researcher)1
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Stefanec, Martin
(Researcher)2
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Rouček, Tomáš
(Researcher)1
-
Ulrich, Jiří
(Researcher)1
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Fedotoff, Laurenz Alexander
(Researcher)2
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Rekabi-Bana, Fateme
(Researcher)3, 4
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Arvin, Farshad
(Project leader)5, 4
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Schmickl, Thomas
(Project leader)2
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Krajník, Tomáš
(Project leader)1
- 1. Faculty of Electrical Engineering, Czech Technical University in Prague
- 2. Artificial Life Lab, Department of Zoology, Institute of Biology, University of Graz
- 3. University of Manchester
- 4. Durham University, Computer Science Department
- 5. Durham University
Description
Dataset Overview
This dataset was developed as part of the research study:
Janota et al., "Non-invasive Honeybee Colony Monitoring via Robotic Mapping of Combs in Observation Hives", Computers and Electronics in Agriculture, 2025. DOI: 10.1016/j.compag.2025.111031
Details regarding data collection procedures are available in the paper referenced above. Due to the large volume of data generated during the study, only part of the datasets is publicly released. The remaining datasets can be provided upon reasonable request. The datasets included in this release are:
- Map Checkpoints: Sets of cells with belief values over considered states, along with a list of all integrated observations in the map.
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Trained Models: Classification models for egg-laying, open brood, capped brood, and cell detection model.
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Annotated Cell Sequences: A collection of 531 cell sequences containing individual cell images and corresponding annotations.
The code for the paper can be found at: https://gitlab.sensorbees.eu/janota/2025_cea_mapping.git
1. Map Checkpoints
The map folder contains an image of the final map, .npz files with the individual map checkpoints and a .csv file with all the integrated observations in the map (open cell, capped brood, egg-laying event).
Individual Map Checkpoints
The individual .npz files contain the following attributes:
| Attribute | Description |
|---|---|
| centers | metric positions of the cells (in meters) |
| counts | a number of detections of the cell |
| sizes | estimated radius of the cell (in pixels) |
| last_prediction_timestamp | UTC timestamp (seconds) of the last prediction step (default=-1 if the cell was not observed yet) |
| last_update_timestamp | UTC timestamp (seconds) of the last update step with observation (default=-1 if the cell was not observed yet) |
| cell_states | belief over the states (43-dimensional vector of values in range (0, 1)) |
List of all integrated observations
The .csv file with all integrated observations has the following columns:
| Column name | Description |
|---|---|
| cell_id | ID of the cell in the map that the observation belongs to |
| det_id | -2 for egg-laying obs., -1 for capped brood obs., >= 0 for open cell obs. |
| u_x | x-position in image (pixels) for capped brood obs., metric x-position (meters) for open cell obs. |
| v_y_conf | y-position in image (pixels) for capped brood obs., metric y-position (meters) for open cell obs., position confidence for egg-laying obs. |
| timestamp | UTC timestamp (seconds) of the obs. |
| obs | confidence of open brood/capped brood/egg-laying classifier |
| bag_name | identifier for the images (not relevant without raw data) |
| scan_id | identifier for the images (not relevant without raw data) |
| img_id | identifier for the images (not relevant without raw data) |
2. Trained Models
We provide the following trained models in the folder models:
- Cell detection (YOLOv5s6)
- Open brood classification (ResNet-9)
- Capped brood classification (ResNet-9)
- Egg-laying event classification (ResNet-9)
3. Annotated Cell Image Sequences
We provide both the test and train cell sequence images in respective folders test_sequences_2024, train_sequences_2024. The folders contain subfolders with names denoting the ID of the cells. Each cell folder contains images (both open cell detections and occluded/capped cell images) and a .csv file with the following columns:
| Column name | Description |
|---|---|
| cell_id | ID of the cell in the map that the observation belongs to |
| det_id | -1 for occluded/capped brood observation, >= 0 for open cell observation |
| x | x-position (pixels) of the cell in the corresponding raw image |
| y | y-position (pixels) of the cell in the corresponding raw image |
| timestamp | UTC timestamp (nanoseconds) when the corresponding image was taken |
| capped_brood | 1 if observation is capped brood, 0 otherwise |
| labels | -2 for capped brood, -1 for unknown, 0 for other, 1 for any open brood, 2 for egg, 3 for larva |
| bag_name | identifier for the images (not relevant without raw data) |
| scan_id | identifier for the images (not relevant without raw data) |
| img_id | identifier for the images (not relevant without raw data) |
Citation
To attribute this dataset in your research, please cite the corresponding paper:
Janota et al., "Non-invasive Honeybee Colony Monitoring via Robotic Mapping of Combs in Observation Hives", Computers and Electronics in Agriculture, 2025. DOI: 10.1016/j.compag.2025.111031
Files
map.zip
Additional details
Related works
- Is supplement to
- Dataset: 10.1016/j.compag.2025.111031 (DOI)
Funding
- European Commission
- SENSORBEES - Sensorbees are ENhanced Self-ORganizing Bio-hybrids for Ecological and Environmental Surveillance 101130325
- UK Research and Innovation
- SENSORBEES: Sensorbees are ENhanced Self-ORganizing Bio-hybrids for Ecological and Environmental Surveillance 10109956
- European Commission
- RoboRoyale - ROBOtic Replicants for Optimizing the Yield by Augmenting Living Ecosystems 964492
- Ministry of Education Youth and Sports
- Robotics and advanced industrial production CZ.02.01.01/00/22 008/0004590
- University of Graz
- Field of Excellence COLIBRI
- Czech Technical University in Prague
- Student Grant SGS22/168/OHK3/3T/13
Dates
- Collected
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2024-08-15/2024-09-09
Software
- Repository URL
- https://gitlab.sensorbees.eu/janota/2025_cea_mapping.git