Development of a Low-Cost Wireless Bee-Hive Temperature and Sound Monitoring System

Precision beekeeping requires data acquisition, data analysis, and applications where the initial phase data on the beehive plays a fundamental role. This method of apiculture could be used to measure different bee colony parameters in real time, leveraging on wireless sensing technologies, which aid monitoring of a bee colony, and enhances the monitoring of infectious diseases like colony collapse disorder–a major loss in the management of honey bee population. In this paper, a low-cost wireless technology-based system, which measures in real-time, the temperature in and around the beehive, and the sound intensity inside the hive is presented. This monitoring system is developed using an Arduino microprocessor, an ESP8266 communication module, and a web-based server. The proposed system provides valuable information concerning the bee colony behavior in terms of temperature variations and sound characteristics. Based on the measured temperature and sound information, colony beekeepers could easily detect events like increased food usage by the bees, breeding start time, pre-swarming action, actual swarming pattern, and the bee colony's death

An IoT-Based Beehive Monitoring System for Real-Time Monitoring of Apis cerana indica Colonies

A study was conducted to monitor the bee activity in the colonies of diferente strengths in real time using an IoT-based device. The in-hive temperature and relative humidity were measured in the colonies of Apis cerana indica Fabricius of different strengths using the sensor-laden IoT device that was correlated with the movement of foragers into and out of the hive. A significantly higher movement of foragers was recorded at an in-hive temperature and relative humidity of 27.84 ºC and 61.47% at 5-6 p.m. with an observed activity of 9,638 bees/hive/hour in the strong colonies. In the weak colonies, the mean forager activity was 1,436.3 bees/hive/hour, which was recorded at an in-hive temperature of 26.52 ºC and 61.42% relative humidity. The mean honey area in the strong and weak colonies were 1,300.80±177.61 cm2 and 508.80±156.84 cm2, respectively. Pollen area in the strong and weak colonies were 447.60±112.08 cm2 and 116.20±66.43 cm2, respectively. In the strong and weak colonies, the area under egg brood was 470±53.06 cm2 and 88.20±36.85 cm2, larvae brood was 583.40±11.04 cm2 and 80.00±24.67 cm2 and sealed brood was 684.20±57.98 cm2 and 102.80±16.59 cm2, respectively. The real-time data on the movement of foragers in the colonies of different strengths enabled us to undertake timely intervention in the maintenance of the bee colonies

Recent developments on precision beekeeping: A systematic literature review

The aim of this systematic review was to point out the current state of precision beekeeping and to draw implications for future studies. Precision beekeeping is defined as an apiary management strategy based on monitoring individual bee colonies to minimize resource consumption and maximize bee productivity. This subject that has met with a growing interest from researchers in recent years because of its environmental implications. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was selected to conduct this review. The literature search was carried out in the Scopus database for articles published between 2015 and 2023, being a very recent issue. After two rounds screening and examination, 201 studies were considered to be analysed. They were classified based on the internal parameters of the hive, in turn divided by weight, internal temperature, relative humidity, flight activity, sounds and vibrations, gases, and external parameters, in turn divided by wind speed, rainfall and ambient temperature. The study also considered possible undesirable effects of the use of sensors on bees, economic aspects and applications of Geographic Information System technologies in beekeeping. Based on the review and analysis, some conclusions and further directions were put forward

Ambient Electromagnetic Radiation as a Predictor of Honey Bee (\u3ci\u3eApis mellifera\u3c/i\u3e) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum R2 scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable

Acquisition, Processing, and Analysis of Video, Audio and Meteorological Data in Multi-Sensor Electronic Beehive Monitoring

In recent years, a widespread decline has been seen in honey bee population and this is widely attributed to colony collapse disorder. Hence, it is of utmost importance that a system is designed to gather relevant information. This will allow for a deeper understanding of the possible reasons behind the above phenomenon to aid in the design of suitable countermeasures. Electronic Beehive Monitoring is one such way of gathering critical information regarding a colony’s health and behavior without invasive beehive inspections. In this dissertation, we have presented an electronic beehive monitoring system called BeePi that can be placed on top of a super and requires no structural modifications to a standard beehive (Langstroth or Dadant beehive), thereby preserving the sacredness of the bee space without disturbing the natural beehive cycles. The system is capable of capturing videos of forager traffic through a camera placed over the landing pad. Audio of bee buzzing is also recorded through microphones attached outside just above the landing pad. The above sensors are connected to a low-cost raspberry pi computer, and the data is saved on the raspberry pi itself or an external hard drive. In this dissertation, we have developed an algorithm that analyzes those video recordings and returns the number of bees that have moved in each video. The algorithm is also able to distinguish between incoming, outgoing, and lateral bee movements. We believe this would help commercial and amateur beekeepers or even citizen scientists to observe the bee traffic near their respective hives to identify the state of the corresponding bee colonies. This information helps those mentioned above because it is believed that honeybee traffic carries information on colony behavior and phenology. Next, we analyzed the audio recordings and presented a system that can classify those recordings into bee buzzing, cricket chirping, and ambient noise. We later saw how a long–term analysis of the intensity of bee buzzing could help us understand the hive’s development through an entire beekeeping season. We also investigated the effect of local weather conditions using 21 different meteorological variables on the forager traffic. We collected the meteorological data from a weather station located on the campus of Utah State University. Through our study, we were able to show that without the use of additional costly intrusive hardware to count the bees, we can use our bee motion counting algorithm to calculate the bee motions and then use the counts to investigate the relationship between foraging activity and local weather. To ensure that our findings and algorithms can be reproduced, we have made our datasets and source codes public for interested research and citizen science communities

Audio-Based Identification of Queen Bee Presence Inside Beehives

Honeybees are essential for the health of people and the planet. They play a key role in the pollination of most crops. The high mortality observed in the last decade, caused by stress factors among which the climate change, have raised the necessity of remote sensing the beehives to help monitor the health of honeybees and better understand this phenomenon. Several solutions have been proposed in the literature, and some of them include the analysis of in-hive sounds. In this scenario, we explore the potential of machine learning methods for queen bee detection using only the audio signal, being a good indicator of the colony state of health. In particular, we experiment support vector machines and neural network classifiers. We consider the effect of varying the audio chunk duration and the adoption of different hyperparameters

Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

Tools are provided to assess the health status of managed honeybee colonies by facilitating further harmonisation of data collection and reporting, design of field surveys across the European Union (EU) and analysis of data on bee health. The toolbox is based on characteristics of a healthy managed honeybee colony: an adequate size, demographic structure and behaviour; an adequate production of bee products (both in relation to the annual life cycle of the colony and the geographical location); and provision of pollination services. The attributes ‘queen presence and performance’, ‘demography of the colony’, ‘in-hive products’ and ‘disease, infection and infestation’ could be directly measured in field conditions across the EU, whereas ‘behaviour and physiology’ is mainly assessed through experimental studies. Analysing the resource providing unit, in particular land cover/use, of a honeybee colony is very important when assessing its health status, but tools are currently lacking that could be used at apiary level in field surveys across the EU. Data on ‘beekeeping management practices’ and ‘environmental drivers’ can be collected via questionnaires and available databases, respectively. The capacity to provide pollination services is regarded as an indication of a healthy colony, but it is assessed only in relation to the provision of honey because technical limitations hamper the assessment of pollination as regulating service (e.g. to pollinate wild plants) in field surveys across the EU. Integrating multiple attributes of honeybee health, for instance, via a Health Status Index, is required to support a holistic assessment. Examples are provided on how the toolbox could be used by different stakeholders. Continued interaction between the Member State organisations, the EU Reference Laboratory and EFSA is required to further validate methods and facilitate the efficient use of precise and accurate bee health data that are collected by many initiatives throughout the EU

Assessing the health status of managed honeybee colonies (HEALTHY-B): a toolbox to facilitate harmonised data collection

Tools are provided to assess the health status of managed honeybee colonies by facilitating further harmonisation of data collection and reporting, design of field surveys across the European Union (EU) and analysis of data on bee health. The toolbox is based on characteristics of a healthy managed honeybee colony: an adequate size, demographic structure and behaviour; an adequate production of bee products (both in relation to the annual life cycle of the colony and the geographical location); and provision of pollination services. The attributes ‘queen presence and performance’, ‘demography of the colony’, ‘in-hive products’ and ‘disease, infection and infestation’ could be directly measured in field conditions across the EU, whereas ‘behaviour and physiology’ is mainly assessed through experimental studies. Analysing the resource providing unit, in particular land cover/use, of a honeybee colony is very important when assessing its health status, but tools are currently lacking that could be used at apiary level in field surveys across the EU. Data on ‘beekeeping management practices’ and ‘environmental drivers’ can be collected via questionnaires and available databases, respectively. The capacity to provide pollination services is regarded as an indication of a healthy colony, but it is assessed only in relation to the provision of honey because technical limitations hamper the assessment of pollination as regulating service (e.g. to pollinate wild plants) in field surveys across the EU. Integrating multiple attributes of honeybee health, for instance, via a Health Status Index, is required to support a holistic assessment. Examples are provided on how the toolbox could be used by different stakeholders. Continued interaction between the Member State organisations, the EU Reference Laboratory and EFSA is required to further validate methods and facilitate the efficient use of precise and accurate bee health data that are collected by many initiatives throughout the EU.info:eu-repo/semantics/publishedVersio

Honey Bee Health

Over the past decade, the worldwide decline in honey bee populations has been an important issue due to its implications for beekeeping and honey production. Honey bee pathologies are continuously studied by researchers, in order to investigate the host–parasite relationship and its effect on honey bee colonies. For these reasons, the interest of the veterinary community towards this issue has increased recently, and honey bee health has also become a subject of public interest. Bacteria, such as Melissococcus plutonius and Paenibacillus larvae, microsporidia, such as Nosema apis and Nosema ceranae, fungi, such as Ascosphaera apis, mites, such as Varroa destructor, predatory wasps, including Vespa velutina, and invasive beetles, such as Aethina tumida, are “old” and “new” subjects of important veterinary interest. Recently, the role of host–pathogen interactions in bee health has been included in a multifactorial approach to the study of these insects’ health, which involves a dynamic balance among a range of threats and resources interacting at multiple levels. The aim of this Special Issue is to explore honey bee health through a series of research articles that are focused on different aspects of honey bee health at different levels, including molecular health, microbial health, population genetic health, and the interaction between invasive species that live in strict contact with honey bee populations