Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

UrBAN: Urban Beehive Acoustics and PheNotyping Dataset

Mahsa Abdollahi, Yi Zhu, Heitor R. Guimarães, Nico Coallier, Ségolène Maucourt, Pierre Giovenazzo, Tiago H. Falk

TL;DR

A practical application of this dataset is demonstrated by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.

Abstract

In this paper, we present a multimodal dataset obtained from a honey bee colony in Montréal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 2000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.

UrBAN: Urban Beehive Acoustics and PheNotyping Dataset

TL;DR

A practical application of this dataset is demonstrated by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.

Abstract

In this paper, we present a multimodal dataset obtained from a honey bee colony in Montréal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 2000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.
Paper Structure (17 sections, 2 equations, 11 figures, 6 tables)

This paper contains 17 sections, 2 equations, 11 figures, 6 tables.

Table of Contents

  1. Background and Summary
  2. Methods
  3. Data Records
  4. Technical Validation
  5. Usage notes
  6. Code availability
  7. Acknowledgements
  8. Author contributions statement
  9. Competing interests

Figures (11)

  • Figure 1: Photos of the (a) rooftop urban apiary in Montreal and (b) audio recording hardware.
  • Figure 2: Histograms of the the number of frames of bees for the year of (a) 2021 and (b) 2022 experiments.
  • Figure 3: Barplots of the number of frames of bees on days of inspections for the year of (a) 2021 and (b) 2022 experiments.
  • Figure 4: Insulation used for beehives overwintering.
  • Figure 5: Varroa mite infestation on (a) August 24th, (b) September 1st, and (c) September 30th, 2022.
  • ...and 6 more figures