## 3 Results and Discussion
### Study Goal
#### 3.2.1 Honeybees
To estimate the traffic using videos, a technique based on object tracking and neural networks called Beevee [103] was used to count the number of bees entering or leaving the hive. Their analysis on hive traffic video and inside hive audio showed that before robbery, the target hive had become weak and an easy target. Other important factors contributing to hive health can include the presence of parasites [28] and exposure to environmental pollutants [67; 68], to name a few. In fact, recent research has shown that honeybees can be used as environmental biindicators for heavymetals and polycyclic aromatic hydrocarbons [104]. The work in [28], for example, showed that honeybee hive acoustics could be used to detect a Varroa mite infestation, whereas the work in [105; 106] showed that hive acoustics could be used to detect certain atmospheric pollutants. Using four features, namely, spectral centroid, peak frequency, frequency standard deviation, and bandwidth (range of predominant frequencies with the threshold of \(-9\) dB), coupled with a machine learning framework could lead to Varroa mite detection [28] with an accuracy of 81%. In [68], in turn, changes in acoustic behaviour were monitored and classified using machine learning models, when hives were exposed to acetone, trichloromethane, glutararic dialdehyde, and ethyl ether, and compared to the control condition of blank air. Using MFCCs (mel-frequency cepstral coefficients, a widely-used feature in audio analytics) as features and principal component analysis (PCA) to reduce the dimensionality to two, clear differences in the beehive sounds in the morning, relative to the afternoon, were seen in the control group. Smoke is also known to generate a defensive response in bees and is used by beekeepers when manually inspecting hives. The work in [39] showed that smoke can have a direct effect on hive acoustics with several harmonics showing up in the low-frequency spectrum (0-500 Hz). In addition to the above factors, researchers have also explored the short-term impact that microwave electromagnetic fields [72] and radio frequency electromagnetic radiation [71] have had on hive acoustics. No significant changes were reported; in [71], the same was also true for temperature and humidity sensor data. Designing a hive monitoring system with multiple sensors, in addition to audio, has also been popularized recently [76; 79]. The work in [76], for example, also included measures of temperature and humidity inside the hive, bee combs' weight, and number of entries to the hive to monitor the colony. The long-term monitoring of the colony showed dramatic weight drops, swarming detected in the audio spectrogram analysis with frequency around 400 Hz, as well as entrance counts and temperature. Lastly, hive acoustic analysis has also been used as a means to better understand bee behaviour. For example, in [75], sound analysis was used to monitor the learning of worker bees (_Apis mellifera_). In this study, bee hives were presented with two odours, where one of them was also presented alongside an electric shock. While bees did not produce any different sound upon odour presentation only, they responded to the electric shock with a strong hissing response defined as the production of loud sounds or vibrations by simultaneous wing movements of several honey bees and emit short (0.5-1.0 s) bursts of broadband-frequency sounds [107]. After learning, many bees were shown to hiss when presented with the odour that was previously paired with the electric shock, even though the shock was no longer being presented, with fewer bees hissing upon presentation of other odours. While hissing is known to be an aposematic signal [107], the authors in [74] showed that Japanese honeybees (_Apis cerana japonica_) also hissed when there was no obvious threat near the colony. In a later study by the same authors [73], they explored the relationship of hissing onset and offset times with sunrise and sunset times, as well as environmental factors.