# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 1 Introduction  
Honeybees (_Apis mellifera_) are one of the most commercially important insects, not only because of their five products, but also because of their pollination activity, which helps cultivation and biodiversity [1]. To ensure their health and well-being, beehive monitoring has been performed manually by beekeepers for many years. During such inspections, beekeepers visually examine colony activity and note the presence of pathogens/ parasites, queen reproduction, colony worker and brood populations, and the amount of food stored. These inspections are typically performed every two weeks during the spring and summer months and are labor-intensive and time consuming for the beekeepers, as well as disruptive for the colonies. More recently, with advances in sensing hardware, cloud computing, and machine learning tools, automated beehive monitoring tools have emerged to overcome these limitations, thus creating the field of precision bekeeping [2]. For example, systems based on beehive acoustics [3, 4, 5, 6], live weight [7, 8, 9], internal temperature [10, 11], and humidity [11], as well as CO\({}_{2}\) monitors [11] or multisensor approaches [7, 8, 11], have been introduced. The weight of a beehive can be used as an indicator of honey production and bee population size, with colony weight changes being reported throughout the day. For example, weight have been reported to decrease during the night and early morning dueto nectar consumption, while it increases with foraging activity during the daytime [12]. The authors in [9] monitored the weight of four colonies and showed that the average weight indicates the amount of stored food (during nectar flow), while the changes could be related to the daily consumption of food. Moreover, swarming activity can be tracked based on the weight change of the hive resulting in a sharp decrease. Temperature stability and regulation in a beehive, in turn, can indicate the colony's adaptive response and health state. It has been found that while the internal temperature affects the health of the bees and brood, the productivity of the hive is also strongly affected by internal hive conditions [13]. Proper thermoregulation inside the hive, for example, can help decrease mortality rates and increase honey production by reducing the internal consumption [14; 15]. Moreover, it has been found that a rise in temperature (from 34 \({}^{\circ}\)C-35 \({}^{\circ}\)C to 37 \({}^{\circ}\)C-38 \({}^{\circ}\)C) could be a signature of swarming [16]. Relative humidity (RH) is another important factor for larvae growth, colony development, and bee behavior, where changes in water transportation and larvae feeding have been reported as a function of ambient humidity, hive temperature, and nectar moisture [17]. The authors in [18] used temperature and humidity sensors to monitor ambient conditions, as well as conditions within the breeding comb and the nectar areas. Their results showed that the humidity of the breeding comb was the highest (about 40% RH) and had less daily fluctuation. Moreover, it is well known that bees communicate within the colony using vibration and acoustic signals [19] generated via the movement of their body, wings, and muscle contractions [20]. For example, specific sounds are generated during mite attacks, by failing queens [21], and during swarming [22; 23]. To this end, the acoustic monitoring of beehives has emerged and is gaining popularity. In a recent literature review of beehive acoustics monitoring [24], several systems were reported showcasing tools for (i) bee activity detection [4; 25; 26; 27], (ii) beehive strength monitoring [28], (iii) queen absence detection [3; 5; 29; 30; 31], (iv) swarming detection [22; 23; 32; 33; 34], (v) pathogen or parasite infestation detection [35], (vi) detection of environmental pollutants and chemicals [36; 37; 38; 39], and (vii) measuring of honeybee reaction to smoke [40], as well as overall beehive monitoring (e.g., identifying normal and abnormal live, swarming duration, bee activity time) [41; 42].