A matter of the beehive sound: Can honey bees alert the pollution out of their hives?

Yu, Baizhong; Xin-qiu, Huang; Sharif, Muhammad Zahid; Jiang, Xinru; Di, Nayan; Liu, Fanglin

doi:10.1007/s11356-022-23322-z

Cited by 5 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results suggest that different compounds do lead to different responses in the bee colony ( Fig. 6 , Tables 3 and 4 ), which further confirms the results of previous studies ( Bromenshenk et al, 2009 ; Sharif et al, 2020 ; Zhao et al, 2021 ; Yu et al, 2022 ), and verifies the applicability of VGGish embedding in bee colony sound classification. As seen from the log spectrum of bee colony sounds ( Fig.…”

Section: Discussionsupporting

confidence: 91%

“…Hive monitoring based on colony sound has made numerous research achievements in recent years ( Terenzi, Cecchi & Spinsante, 2020 ; Kim, Oh & Heo, 2021 ; Zhao et al, 2021 ; Soares et al, 2022 ; Yu et al, 2022 ) and has become increasingly popular with many international companies such as Arnia, Bee Hero, Nectar, and Broodminder ( https://www.umt.edu/bee/monitoringconference_2020/ ).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification

Sharif

et al. 2023

PeerJ

Self Cite

View full text Add to dashboard Cite

Background Bee colony sound is a continuous, low-frequency buzzing sound that varies with the environment or the colony’s behavior and is considered meaningful. Bees use sounds to communicate within the hive, and bee colony sounds investigation can reveal helpful information about the circumstances in the colony. Therefore, one crucial step in analyzing bee colony sounds is to extract appropriate acoustic feature. Methods This article uses VGGish (a visual geometry group—like audio classification model) embedding and Mel-frequency Cepstral Coefficient (MFCC) generated from three bee colony sound datasets, to train four machine learning algorithms to determine which acoustic feature performs better in bee colony sound recognition. Results The results showed that VGGish embedding performs better than or on par with MFCC in all three datasets.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification

Sharif

et al. 2023

PeerJ

Self Cite

View full text Add to dashboard Cite

show abstract

“…The absence of eggs or larvae signals potential issues-the queen bee might be deceased, unwell, or absent from the hive. Regular checks are crucial for maintaining a thriving bee colony and addressing any concerns promptly [8]. In critical scenarios, the entire swarm's survival is at risk, necessitating swift intervention.…”

Section: Introduction 1surveillance and Assessment Of Bee Colony Healthmentioning

confidence: 99%

“…This approach extends beyond air quality, as hive sounds can effectively monitor pollution in water and soil. The ability to utilize beehive sounds for multi-environmental pollution monitoring highlights its versatility in safeguarding the wellbeing of bee colonies and environmental ecosystems [8]. Assessing the health status of managed honey bee colonies is a complex, integral, and multi-factorial process, where all parameters are strongly connected, in both ways-from the hive to the environment and the other way around.…”

Section: Introduction 1surveillance and Assessment Of Bee Colony Healthmentioning

confidence: 99%

Biomonitoring: Developing a Beehive Air Volatiles Profile as an Indicator of Environmental Contamination Using a Sustainable In-Field Technique

Ilić,

Brkić,

Sekulić

2024

Sustainability

View full text Add to dashboard Cite

The wellbeing of the honey bee colonies and the health of humans are connected in numerous ways. Therefore, ensuring the wellbeing of bees is a crucial component of fostering sustainability and ecological harmony. The colony collapse disorder (CCD) phenomenon was first reported in 2006 when the majority of bee colonies in Europe died out, due to an increase in infections, contamination of hives with agrochemical pesticides, and persistent organic pollutants (POPs). Only 6 years after the emergence of CCD, more than 6.5 million premature deaths were reported, as a consequence of persistent human exposure to air pollution. The insect species such as the honey bee Apis mellifera L. and the air matrix inside the beehive can be used as tools in biomonitoring, instead of traditional monitoring methods. This may have advantages in terms of cost-effective bioindicators of the environmental health status, showing the ability to record spatial and temporal pollutant variations. In this study, we present the sustainable in-field usage of the portable membrane inlet mass spectrometry (MIMS) instrument for an instant and effective determination of the level of environmental pollution by analytical identification of hive atmosphere volatile organic compound (VOC) contaminants, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), monocyclic aromatic hydrocarbons (BTEX) compounds, and pesticides. The samples were taken from hives located in urbanized and rural regions, highlighting variations in contamination. The MIMS results were benchmarked against a conventional laboratory sampling technique, such as GC-MS.

show abstract

“…The "beehive sound", or "colony noise" is the collective buzzing and vibrating sound produced by the activity of honey bees within the hive. Characterized as a series of low-frequency vibrations and higher-frequency buzzing noises, the beehive sound has been proven to strongly correlate with the colony size, activity level, and environmental conditions (Kirchner, 1993;Sharif et al, 2020;Terenzi et al, 2020;Yu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Identifying foraging spatial cues in beehive sound activity using machine learning methods

Bocanegra,

Galvez,

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

The beehive sound, a continuous signal produced by bees within the hive, has been found to correlate with different behavioral states of the colony, like being queenless and swarming. We investigated the possibility of identifying foraging spatial cues in this signal. We recorded a colony’s sound while foraging from food sources located at three different distances from the hive, one at a time. The recordings were split into frames to obtain six statistics of their Mel Frequency Cepstral Coefficients. Then, we evaluated different autoencoding networks to obtain a latent space that allowed frames from different foraging distances to be easily differentiable. The high accuracy, silhouette score, and F1 score shown in the obtained latent spaces strongly support our approach for identifying foraging spatial cues in beehive sound activity.

show abstract

A matter of the beehive sound: Can honey bees alert the pollution out of their hives?

Cited by 5 publications

References 48 publications

Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification

Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification

Biomonitoring: Developing a Beehive Air Volatiles Profile as an Indicator of Environmental Contamination Using a Sustainable In-Field Technique

Identifying foraging spatial cues in beehive sound activity using machine learning methods

Contact Info

Product

Resources

About