Audio-based Identification of Beehive States

Nolasco, Ines; Terenzi, Alessandro; Cecchi, Stefania; Orcioni, Simone; Bear, Helen L.; Benetos, Emmanouil

doi:10.1109/icassp.2019.8682981

Cited by 58 publications

(61 citation statements)

References 19 publications

(22 reference statements)

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“…In 2019–2020, several works have been published on bee sound analysis. Nolasco et al [ 51 ] applied ML techniques to analyze honey bees’ sound and detect the queen bee presence. Data from the Nu-Hive project [ 52 ] were used, analyzing sound from two different colonies in normal and orphaned situations.…”

Section: Recent Resultsmentioning

confidence: 99%

“…In ( e ), accelerometers placement proposed in [ 43 ] is presented: the sensors exploit vibrations and do not suffer from propolization problems. ( f ) belongs to approaches presented in [ 51 , 52 , 57 , 58 ]: the microphones are hidden inside the hive walls, and a grid is used to protect them. ( g ) from [ 54 ] shows a custom brood frame.…”

Section: Figurementioning

confidence: 99%

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On the Importance of the Sound Emitted by Honey Bee Hives

Terenzi

Cecchi

Spinsante

2020

Veterinary Sciences

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Recent years have seen a worsening in the decline of honey bees (Apis mellifera L.) colonies. This phenomenon has sparked a great amount of attention regarding the need for intense bee hive monitoring, in order to identify possible causes, and design corresponding countermeasures. Honey bees have a key role in pollination services of both cultivated and spontaneous flora, and the increase in bee mortality could lead to an ecological and economical damage. Despite many smart monitoring systems for honey bees and bee hives, relying on different sensors and measured quantities, have been proposed over the years, the most promising ones are based on sound analysis. Sounds are used by the bees to communicate within the hive, and their analysis can reveal useful information to understand the colony health status and to detect sudden variations, just by using a simple microphone and an acquisition system. The work here presented aims to provide a review of the most interesting approaches proposed over the years for honey bees sound analysis and the type of knowledge about bees that can be extracted from sounds.

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Section: Recent Resultsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

On the Importance of the Sound Emitted by Honey Bee Hives

Terenzi

Cecchi

Spinsante

2020

Veterinary Sciences

Self Cite

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“…So far, two feature sets are used for the classification of beehive audios. They are the low-level signal feature set, including Zero-Crossing Rate, Bandwidth, Spectral Centroid, and Signal Energy (Qandour et al, 2014), and the Mel spectra set, which are dozens of Mel Frequency Cepstral Coefficients (MFCC) (Robles-Guerrero et al, 2017) (Nolasco et al, 2019). Recently, ecologists have been developing soundscape indices to assess biodiversity at landscape scale (Mammides et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Soundscape Indices: New Features for Classifying Beehive Audio Samples

Sharif

Wario

et al. 2020

Sociobiology

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As the study of honey bee health has gained attention in the biology community, researchers have looked for new, non-invasive methods to monitor the health status of the colony. Since the beehive sound alters when the colony is exposed to stressors, analysis of the acoustic response of the colony has been used as a method to identify the type of stressor, whether it is chemical, pest, or disease. So far, two feature sets have been successfully used for this kind of analysis, being these low-level signal features and Mel Frequency Cepstral Coefficients (MFCC). Here we propose using soundscape indices, developed initially to delineate acoustic diversity in ecosystems, as an alternative to now used features. In our study, we examine the beehive acoustic response to trichloromethane laced-air and blank air and compare the performance of all three feature sets to discern the colony's sound between the hive being exposed to the chemical and not. Our results show that sound indices overperform the alternative features sets on this task. Based on these findings, we consider sound indices to be a valid set of features for beehive sound analysis and present our results to call the attention of the community on this fact.

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“…Several deep learning algorithms were compared in [19] with the standard machine learning approaches, with promising results for real-life applications. In [20], the authors compared SVM and Convolutional Neural Networks (CNNs) for recognizing the beehive status from audio signals. The authors reported that the SVM approach provided a better generalization effect over the CNN.…”

Section: Introductionmentioning

confidence: 99%

Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service

Žgank

2019

Sensors

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Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models' and Gaussian Mixture Models' topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.In the case of mobile networks, the 5G, and the already existing LTE-M1/NB-IoT, enable ubiquitous connectivity, which is important when the system gets distributed over more than one location. The increased number of connected devices, many of them originating from wireless-distributed sensor networks, were also responsible for the last major changes in network protocol standardization, where IPv6 was defined and deployed to communication networks, with the objective to cope with the new situation.If IoT and intelligent ambiance present one of the upfront sectors, agriculture is still a more traditional one. ICT technologies were introduced widely [6] to agriculture in the last two decades, but there is a large area of possibilities on how, additionally, to include real-time monitoring, machine learning and sensor networks into this sector. Sensor networks enable numerous solutions for smart farming [7], intending to increase the production rate and improve sustainability, but they are also, in parallel, in charge of taking care of animal condition and health. Beekeeping is an important part of agriculture, where the distributed locations are frequently present, and thus, indicate the need to ease the monitoring of animals in a 24/7 mode, which can benefit from advanced intelligent ambiance technologies. The popularity of modern urban beekeeping initiatives has gained further momentum to this research field, and presents an additional possibility as to how to include digital agriculture into a smart city and smart home solutions [8].Swarming has always been seen by beekeepers as an extremely important event, which requires an immediate response. Beekeepers were used to observing the beeh...

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Audio-based Identification of Beehive States

Cited by 58 publications

References 19 publications

On the Importance of the Sound Emitted by Honey Bee Hives

On the Importance of the Sound Emitted by Honey Bee Hives

Soundscape Indices: New Features for Classifying Beehive Audio Samples

Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service

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