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Janus: A Combined Radar and Vibration Sensor for Beehive Monitoring

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A novel beehive monitoring sensor with two faces is described. This sensor is attached to the outside of a hive, near the hive entrance. The outward-looking sensor is a 24 GHz continuous-wave Doppler radar for monitoring bee flying activity. The inward-looking sensor is a piezo-electric transducer. Unlike a conventional microphone that would pick up the sounds bees make, the piezo-electric transducer picks up the incidental vibrations transmitted by bee activity to the hive structure. The root-mean-square powers in concurrent radar and vibration measurements are shown to be highly correlated during honeybee swarming and robbing events. Principal component analysis was applied to radar, vibration, and environmental measurements to further reduce false alarms.
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VOL. 5, NO. 3, MARCH 2021 1500204
Sensor phenomena
Janus: A Combined Radar and Vibration Sensor for Beehive Monitoring
Herbert M. Aumann1* , Margery K. Aumann2, and Nuri W. Emanetoglu1**
1Department of Electrical and Computer Engineering, The University of Maine, Orono, ME 04469 USA
2Dover-Foxcroft, ME 04426 USA
*Life Member, IEEE
**Member, IEEE
Manuscript received January 8, 2021; revised January 28, 2021; accepted January 31, 2021. Date of publication February 3, 2021; date of current version
February 26, 2021.
Abstract—A novel beehive monitoring sensor with two faces is described. This sensor is attached to the outside of a hive,
near the hive entrance. The outward-looking sensor is a 24-GHz continuous-wave Doppler radar for monitoring bee flying
activity. The inward-looking sensor is a piezoelectric transducer. Unlike a conventional microphone that would pick up
the sounds bees make, the piezoelectric transducer picks up the incidental vibrations transmitted by bee activity to the
hive structure. The root-mean-square powers in concurrent radar and vibration measurements are shown to be highly
correlated during honeybee swarming and robbing events. Principal component analysis was applied to radar, vibration,
and environmental measurements to further reduce false alarms.
Index Terms—Sensor phenomena, beehive monitoring, Doppler radar, piezoelectric vibration transducer.
There are two unusual events in an apiary that require immediate
intervention by the beekeeper: swarming and robbing.
Swarming is a natural event during which about half the bee popu-
lation leaves the hive to establish a new colony. In as much as possible,
beekeepers like to avoid swarming, and its associated loss of honey
production, using proper management techniques [1]. When swarming
happens unexpectedly, however, the beekeeper would like to be alerted
as soon as possible. Swarming may take place in a matter of minutes.
It usually starts with a great uproar from the beehive, followed by
a rapid disgorgement and flight of thousands of bees in a cloud. The
swarm initially lands and forms a tight cluster close to the original hive
before flying off to its final destination. This bivouac may last for as
little as an hour, or depending on the weather, it may last for days [2].
Quick action during the bivouac phase is the beekeeper’s best chance
of capturing a swarm and thereby increasing his apiary stock [3].
At times of poor honey flow, bees from a stronger hive may attack a
weaker hive and steal its honey [4]. If successful, robbers can clean all
the honey out of a hive in a few days. Robbing tends to be a persistent,
all-day event that is usually accompanied by a high level of noise and
continuous frenzied flying activity in front of the hive [3]. One way of
dealing with that situation is either to reduce temporarily the size of
the hive entrance or to close it altogether [3].
A commonly occurring and harmless activity is the “mass orienta-
tion flight,” a bee activity that has so far eluded a concise biological
explanation [5]. In this scenario, a substantial cloud of bees flies back
and forth in front of the hive entrance. Mass orientation flights have
often been mistaken for swarming, except that the bees do not fly far
away from the hive, and they return to the hive after a few minutes.
The level of flying activity of orientation flights and swarming can
be very similar; however, the level of acoustic noise associated with
Corresponding author: Herbert M. Aumann (e-mail: herbert.aumann@maine.
Associate Editor: S.-R. Kothapalli.
This work was supported by the Entirely Self-Funded by the Authors.
Digital Object Identifier 10.1109/LSENS.2021.3056870
Fig. 1. Radar/vibration sensor installed on beehive 8B.
orientation flights is considerably lower. Although orientation flights
are also detected by our sensor, they usually do not require intervention
by the beekeeper.
By carefully noting flight activity near the beehive entrance and
acoustic noise from the hive, an experienced beekeeper can tell very
quickly if the bees are happy, sad, or mad [6]. However, it is often not
practical for the beekeeper to check on hives in a remote apiary every
day. An inexpensive beehive monitoring system is of interest to that
would warn the beekeeper that a swarm is about to take place, or has
just occurred, or that a robbery is in progress.
An external sensor, as shown in Fig. 1, can meet the aforementioned
requirement. We will show that the observations of swarming events,
done both with a Doppler radar and with a vibration sensor, are
highly correlated, even though they are based on entirely different
phenomenology. When a major hive perturbation occurs, and the
Doppler and vibration levels are both high, then it is likely to be a
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
1500204 VOL. 5, NO. 3, MARCH 2021
Fig. 2. Radar/vibration sensor block diagram.
swarm. When the levels differ significantly, then the event is most
likely due either to robbing or to an “orientation flight.”
We review prior related work in Section II. The sensor hardware
is described in Section III. In Section IV, we verify the operation of
the two-faced sensor, and in Section V, we show some experimental
The unique sounds that bees make have been described since an-
tiquity [3]. We are all familiar with the buzz of bee wings, which is
typically at 270 Hz. At other times, one can hear intermittent sounds,
such as queen bees tooting (550 Hz) or quacking (300 Hz), or workers
piping (400 Hz) [7]–[9]. Most bee sounds are well below 600 Hz.
Numerous sensors have been developed for collecting, remotely
and in real-time, acoustic signals from bees within a beehive [10]–
[17]. A review of such sensors can be found in [17]. The installation
requires opening the hive and inserting a microphone between the
frames. Eventually, the microphone becomes covered with propolis
and thereby rendered less effective. Interpretation of the bee sounds
is left to the beekeeper or else requires expensive offline equipment
for the needed feature extraction. Sounds captured with an acoustic
microphone outside the hive [17] while easier to achieve have often
been found to be corrupted by environmental interference.
Bee behavior has also been studied by measuring localized vibra-
tions with an accelerometer. In such studies, the accelerometer was
installed either inside the hive [4], [18] or in the hive wall [19].
In an entirely different approach, we demonstrated the use of a low-
power continuous-wave (CW) Doppler radar to listen to bee sounds
[20] or to monitor flying activity outside the beehive [21]–[23]. In
this letter, we combine this radar sensor with a vibration sensor in a
two-faced apparatus to improve swarm detection.
We have named our sensor “Janus” after the god of Roman mythol-
ogy. Janus is usually pictured with two, sometimes dissimilar faces,
one looking forward and one looking backward. Similarly, our system
contains two sensors, looking in opposite directions. A block diagram
of the system is shown in Fig. 2. One sensor monitors bee activity
inside the beehive (red), and one sensor monitors activity outside the
beehive (blue). A Janus sensor implementation is depicted in Fig. 3.
A. Vibration Sensor
The vibration sensor is based on a very inexpensive 26-m-diameter
piezoelectric transducer commonly used in acoustic guitar pickups. It
is firmly attached to the outside wall of a wooden Langstroth hive above
Fig. 3. Janus front end.
Fig. 4. Vibration sensor impulse response.
Fig. 5. Relative signal strength of vibration (red) and radar (blue)
sensors after amplification.
the hive entrance. The transducer is connected to an audio amplifier
with about 70-dB gain and a 300–2000 Hz frequency response, as
Careful observations reveal that the frequency of the signal mea-
sured by such a sensor installation is primarily determined by the
resonant characteristics of the wooden hive and its content. It has very
little to do with the classic “bee sounds” described in Section II. It
is a by-product of thousands of bee feet “strumming the guitar, as it
were. We verified the response of an empty hive by striking it with a
suspended ball bearing. The data processing is described in Section IV.
Consistent with the theoretical acoustic cutoff frequency, a completely
empty hive has a resonance at about 360 Hz. We then repeated the test
with a hive filled with ten empty frames. As illustrated in Fig. 4, the
resonant frequency of the full hive has shifted to about 1100 Hz.
VOL. 5, NO. 3, MARCH 2021 1500204
Fig. 6. Vibration and radar FTI plots.
B. Doppler Radar
The motion detector, shown in Fig. 3, is a 24-GHz CW Doppler
radar found in automobile collision avoidance systems. This sensor
has been used successfully to observe bee flying activity [23]. No
adverse radiation effects on bees were observed from an active cell
phone inserted into a bee hive [24]. The present radar is outside the
hive and transmits 1/100 power of a cell phone. A similar 10.5-GHz
sensor was described in [22]. However, due to the much higher radar
cross section of bees at 24 GHz, such a radar has significantly better
sensitivity [22].
The departure of foraging bees from the hive entrance causes the
Doppler frequency of the 24-GHz CW radar to shift from 0 Hz to about
800 Hz over a distance of about 2 m [23]. When using the same audio
amplifier as above, the corresponding 1/R4losses distort the effective
signal amplification, as shown in Fig. 5 in blue.
Two different data collection and processing systems were used with
the same Janus front end.
A. Hard-Wired Sensor
In the initial system, the Janus front end was directly connected from
a beehive to the stereo microphone input of a laptop computer. Raw
12-b Analog-to-digital converter (ADC) samples at 8 ksamples/s were
collected from both, the radar and vibration sensor. These samples
were recorded for 20 s every 2 min from sunrise to sunset. Frequency-
time-intensity (FTI) plots were generated with MATLAB for each 20-s
data collection. An example is given in Fig. 6.
While the radar FTI shows the Doppler of individual bees coming
and going, the vibration FTI from thousands of bees is considerably
more uniform. In Fig. 7, we averaged the FTI’s of both channels over
the 20-s data collection. The plot shows remarkable similarity to the
expected frequency response shown in Fig. 5.
It was shown in [21] that the root-mean-squared (rms) value of the
raw Doppler signal is equivalent to the total power in the Doppler
spectrum and is a good indicator of the level of bee activity. We
similarly calculated the rms value of the vibrational signal. The rms
Fig. 7. Average frequency response of Fig. 6.
Fig. 8. Remote bee activity sensor.
values in Fig. 7 were calculated directly from raw ADC samples
without spectral analysis.
B. Remote Sensor
The second sensor version, for remote data collection from multiple
hives, was built with commercial break-out boards, as shown in Fig. 8.
Here, a solid-state timer actuated a microprocessor approximately
every 2 min. The microprocessor then collected 2 s of 12-b radar and
vibration samples and calculated their rms values. These data were
time tagged and transmitted over a 433-MHz wireless link to a base
station for recording on a laptop computer. Up to nine sensors could
be monitored from a distance of almost 200 m. Since the transmission
duty factor is only 0.02%, message collisions occurred only very rarely
and were easily identified by checksum error.
Radar and vibration measurements with identical wireless sensors
were made on three beehives, identified as 6B, 7B, and 8B, located in
Dover-Foxcroft, ME, USA. The hives were continuously monitored
from July 2020 to October 2020.
A. Daily Measurements
Representative examples of days with unusual events are shown in
Fig. 9. All events were visually confirmed. Radar and vibration rms
levels for a swarming event [see Fig. 9(a)] were very high and highly
correlated (ρ>0.5). No significant lag between vibration and radar
data could be observed. As expected, the vibrational level was higher
during a robbing situation [see Fig. 9(b)] and lasts for most of the day.
Although the radar signals in Fig. 9(a) and (c) are similar, correlation
between radar and vibrational signals for “orientation flights” was very
1500204 VOL. 5, NO. 3, MARCH 2021
Fig. 9. Examples of (a) swarming, (b) robbing, and (c) orientation
Fig. 10. PCA analysis of hive radar and vibration data.
B. Seasonal Measurements
Observations over the course of the season indicated that unusual
activities did not always match the ideal cases illustrated in Fig. 9,
leading to false alarms. Other factors, such as ambient temperature and
solar radiation [22], also influence the level of activity. We explored
the correlation between radar, vibration, temperature, and solar flux by
principal component analysis (PCA). The PCA analysis was carried
out in MATLAB on a laptop attached to the base station. Each vertical
line in Fig. 10 represents the magnitude of the most significant principal
component derived from daily data, as exemplified in Fig. 9.
As an experiment, for one hive (8B), the space available for bee pop-
ulation expansion was limited on purpose to induce swarming. Indeed,
the hive swarmed five times. PCA analysis clearly and unambiguously
identified these events, allowing the swarms to be captured. We noticed
a reduced level of activity about ten days before the primary swarm.
This reduced activity level has been reported elsewhere [25], but it is
difficult to detect automatically or in advance.
Both the radar and vibration sensor, when mounted on the outside
wall of a beehive, are capable of detecting swarming and robbing activ-
ity. While the vibration sensor by itself is considerably less expensive,
the detection performance can be improved (i.e., false alarms reduced)
by combining measurements from both sensors and subjecting them
to a PCA.
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... Other monitoring technologies: Audio, video, and other monitoring technologies such as gas sensors (Szczurek et al., 2019a), thermal imaging (Klein et al., 2014), infrared sensors (Shaw et al., 2010), GPS (Ntawuzumunsi et al., 2021), accelerometers (Kontogiannis, 2019), vibration detectors (Aumann et al., 2021), and light intensity sensors (Keppner and Jarau, 2016) are some less standard technologies we identified for Operational monitoring. Although they provide valuable data, perhaps their cost and complexity inhibits widespread adoption. ...
... Recent approaches to detecting this event include the combination of temperature, sound, hive weight, and humidity Hong et al., 2020;Anand et al., 2018). Researchers have investigated too how vibration (Aumann et al., 2021;Ramsey et al., 2020) and radar sensors (Aumann et al., 2021) can be used to detect swarming events. ...
... Recent approaches to detecting this event include the combination of temperature, sound, hive weight, and humidity Hong et al., 2020;Anand et al., 2018). Researchers have investigated too how vibration (Aumann et al., 2021;Ramsey et al., 2020) and radar sensors (Aumann et al., 2021) can be used to detect swarming events. ...
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... The benefits of remote prediction of swarm events include inspecting hives only when needed rather than regularly (Aumann et al., 2021). Furthermore, the data collected can indicate when honey flow stops and colonies need to be relocated or fed to maintain health or being able to find sites with higher honey yields . ...
... In Aumann et al. (2021), a similar approach was used to monitor rather simple activity events of flying honey bees. Swarming, robbing and orientation flights could be indicated with root-mean-squared counts, also known as the quadratic mean (Jones, 2018), which correlates with the total power in the Doppler spectrum. ...
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... The data captured from these kinds of new advanced systems will be processed, and the extracted important features enable beekeepers monitoring beehives remotely without interrupting the bees' natural behavior at their hives. Aumann et al. (2021) introduced a system that combines both radar (for monitoring bee flying activity) and vibration sensor (for picking up incidental vibrations transmitted by bee activity to the hive structure). For many other systems introduced in the literature, one can refer to IoBee, 2 OSBeehives, 3 Hivemind, 4 Gobuzzr, 5 and SAMS (Wakjira et al. 2021). ...
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The widespread decline of honey bee (Apis mellifera L.) colonies registered in recent years has raised great attention to the need of gathering deeper knowledge about this phenomenon, by observing the colonies’ activity to identify possible causes, and design corresponding countermeasures. In fact, honey bees have well-known positive effects on both the environment and human life, and their preservation becomes critical not only for ecological reasons, but also for the social and economic development of rural communities. Smart sensor systems are being developed for real-time and long-term measurement of relevant parameters related to beehive conditions, such as the hive weight, sounds emitted by the bees, temperature, humidity, and CO 2 inside the beehive, as well as weather conditions outside. This paper presents a multisensor platform designed to measure the aforementioned parameters from beehives deployed in the field, and shows how the fusion of different sensor measurements may provide insights on the status of the colony, its interaction with the surrounding environment, and the influence of climatic conditions.
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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.
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Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing samples were separated from the validation samples by beehive and location, a shallower raw audio convolutional neural network with a custom layer outperformed three deeper raw audio convolutional neural networks without custom layers and performed on par with the four machine learning methods trained to classify feature vectors extracted from raw audio samples. On a more challenging dataset of 12,914 audio samples where the training and testing samples were separated from the validation samples by beehive, location, time, and bee race, all raw audio convolutional neural networks performed better than the four machine learning methods and a convolutional neural network trained to classify spectrogram images of audio samples. A trained raw audio convolutional neural network was successfully tested in situ on a low voltage Raspberry Pi computer, which indicates that convolutional neural networks can be added to a repertoire of in situ audio classification algorithms for electronic beehive monitoring. The main trade-off between deep learning and standard machine learning is between feature engineering and training time: while the convolutional neural networks required no feature engineering and generalized better on the second, more challenging dataset, they took considerably more time to train than the machine learning methods. To ensure the replicability of our findings and to provide performance benchmarks for interested research and citizen science communities, we have made public our source code and our curated datasets.
Conference Paper
Swarming of honeybees is one of the biggest problems for the hobbyist-beekeepers. This is a state when old queen bee leaves the hive with worker bees. The aim of this work is to find relation between sound and swarming. The data was collected during the late spring and early summer using microphone that was placed inside the hive. Using the Welch method, implemented in Python, it was proven the relation between the power and swarming.
Honey bees are of vital importance to global crop production. Colony losses have reached historic levels in Europe and North America and are high in other parts of the world. The decline in honey bee health has resulted in the demand for novel mechanisms of monitoring colony health by beekeepers and researchers. Methods of monitoring bee health traditionally involve opening of the hive either for manual data collection or the use of invasive electronic monitors. This study evaluates a beehive activity monitor based on the Doppler radar principle as a tool for assessing honey bee colony health. The research was conducted during a two-year study (2017–2018). We discuss the development of a portable Doppler radar unit and three experiments conducted with the aim of showing its utility in monitoring colony health. First we determined the relationship between: (I) forager activity and colony health (r² = 0.433, P = 0.006), (II) Doppler unit root mean square (RMS) and forager activity (r² = 0.766, P = 0.013), and then (III) Doppler unit RMS and colony health measured as total sum of brood and worker population (r² = 0.731, P = 0.026). This small portable Doppler unit is solar powered and can be deployed in any apiary to provide beekeepers with a tool for tracking their colonies' health in real time.
Mobile phones can be found almost everywhere across the globe, upholding a direct point-to-point connection between the device and the broadcast tower. The emission of radiofrequency electromagnetic fields (RF-EMF) puts the surrounding environment inevitably into contact with this radiation. We have therefore exposed honey bee queen larvae to the radiation of a common mobile phone device (GSM band at 900 MHz) during all stages of their pre-adult development including pupation. After 14 days of exposure, hatching of adult queens was assessed and mating success after further 11 days, respectively. Moreover, full colonies were established of five of the untreated and four of the treated queens to contrast population dynamics. We found that mobile phone radiation had significantly reduced the hatching ratio but not the mating success. If treated queens had successfully mated, colony development was not adversely affected. We provide evidence that mobile phone radiation may alter pupal development, once succeeded this point, no further impairment has manifested in adulthood. Our results are discussed against the background of long-lasting consequences for colony performance and the possible implication on periodic colony losses.