DOI: 10.5281/zenodo.17313414
title: [Dataset] Non-invasive Honeybee Colony Monitoring via Robotic Mapping of Combs in Observation Hives

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.



Trained Models: Classification models for egg-laying, open brood, capped brood, and cell detection model.




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