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

Wireless Data Acquisition For Apiology Applications

Colony Collapse Disorder (CCD), a disease affecting honey bee colonies, is a problem threatening the food security and economy of the entire world. Discovering the cause of CCD is particularly difficult because of the variety of colony locations and environmental variables. In addition, CCD instances do not tend to follow an easily recognizable pattern with respect to apiary conditions, which is exacerbated by the subjective nature of manual apiary data recording methods. Traditional monitoring methods are typically too expensive for wide-scale deployment and often require manual collection of the data, reducing the quantity of data available for analysis. A general wireless data acquisition system was designed to improve the quantity and quality of data and to explore general issues related to wireless data acquisition systems. The system was constructed using off-the-shelf -components to reduce cost. The acquisition system and data management tools were programmed using freely available tools and software. Beehive data are transmitted to the Internet wirelessly through the use of a cellular GSM modem. Results show that it is feasible to build an economical, general purpose wireless data acquisition system that can gather quality data for an Apiology application with similar capabilities to higher-cost contemporary systems

Wireless sensor networks, actuation, and signal processing for apiculture

Recent United Nations reports have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator for a wide range of crops. Protecting the honey bee population worldwide, and enabling them to maximise productivity, are important concerns. This research proposes a framework for addressing these issues by considering an inter-disciplinary approach, combining recent developments in engineering and honey bee science. The primary motivation of the work outlined in this thesis was to use embedded systems technology to improve honey bee health by developing state of the art in-hive monitoring systems to classify the colony status and mechanisms to influence hive conditions. Specific objectives were identified as steps to achieve this goal: to use Wireless Sensor networks (WSN) technology to monitor a honey bee colony in the hive and collect key information; to use collected data and resulting insights to propose mechanisms to influence hive conditions; to use the collected data to inform the design of signal processing and machine learning techniques to characterise and classify the colony status; and to investigate the use of high volume data sensors in understanding specific conditions of the hive, and methods for integration of these sensors into the low-power and low-data rate WSN framework. It was found that automated, unobtrusive measurement of the in-hive conditions could provide valuable insight into the activities and conditions of honey bee colonies. A heterogeneous sensor network was deployed that monitored the conditions within hives. Data were collected periodically, showing changes in colony behaviour over time. The key parameters measured were: CO2, O2, temperature, relative humidity, and acceleration. Weather data (sunshine, rain, and temperature) were collected to provide an additional analysis dimension. Extensive energy improvements reduced the node’s current draw to 150 µA. Combined with an external solar panel, self-sustainable operation was achieved. 3,435 unique data sets were collected from five test-bed hives over 513 days during all four seasons. Temperature was identified as a vital parameter influencing the productivity and health of the colony. It was proposed to develop a method of maintaining the hive temperature in the ideal range through effective ventilation and airflow control which allow the bees involved in the activities above to engage in other tasks. An actuator was designed as part of the hive monitoring WSN to control the airflow within the hive. Using this mechanism, an effective Wireless Sensor and Actuator Network (WSAN) with Proportional Integral Derivative (PID) based temperature control was implemented. This system reached an effective set point temperature within 7 minutes of initialisation, and with steady state being reached by minute 18. There was negligible steady state error (0.0047%) and overshoot of <0.25 °C. It was proposed to develop and evaluate machine learning solutions to use the collected data to classify and describe the hive. The results of these classifications would be far more meaningful to the end user (beekeeper). Using a data set from a field deployed beehive, a biological analysis was undertaken to classify ten important hive states. This classification led to the development of a decision tree based classification algorithm which could describe the beehive using sensor network data with 95.38% accuracy. A correlation between meteorological conditions and beehive data was also observed. This led to the development of an algorithm for predicting short term rain (within 6 hours) based on the parameters within the hive (95.4% accuracy). A Random Forest based classifier was also developed using the entire collected in-hive dataset. This algorithm did not need access to data from outside the network, memory of previous measured data, and used only four inputs, while achieving an accuracy of 93.5%. Sound, weight, and visual inspection were identified as key methods of identifying the health and condition of the colony. Applications of advanced sensor methods in these areas for beekeeping were investigated. A low energy acoustic wake up sensor node for detecting the signs of an imminent swarming event was designed. Over 60 GB of sound data were collected from the test-bed hives, and analysed to provide a sound profile for development of a more advanced acoustic wake up and classification circuit. A weight measuring node was designed using a high precision (24-bit) analogue to digital converter with high sensitivity load cells to measure the weight of a hive to an accuracy of 10g over a 50 kg range. A preliminary investigation of applications for thermal and infrared imaging sensors in beekeeping was also undertaken

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 assessment of stingless beehive climate impact using multivariate recurrent neural networks

A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation

Remote monitoring of beehive activity

Colonies of the European honeybee, the most important pollinator insects, are subjected to many negative impacts of modern society, in form of pollution, pests, invading species and colony collapse disorder (CCD). The newly emerging technology of the Internet of Things (IoT), enables remote monitoring using wireless sensors inside beehives. In this paper, we present the model of the system for remote temperature monitoring on a various points inside the beehive colony using IoT devices. Measured data is transmitted to a remote server where received data streams are processed in real-time using Complex Event Processing (CEP) which enables detection of critical events and report it to the beekeeper. These data streams are compared with reference temperature patterns using machine learning algorithms, which give computers the ability to learn to detect events without being explicitly programmed. This system can significantly reduce the beekeeper reaction time and increase chances for a beehive colony overcoming certain types of anomalies with human intervention.Publishe

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

Embedded wireless stingless beehive monitoring and data management system

In this paper, an embedded wireless stingless bee monitoring system, which investigates the environment's temperature and humidity effect on the bee activity and honey production of Heterotrigona Itama, a stingless bee species, is presented. The variables observed by the system are the weight of the honey container, the temperature inside the hive, humidity inside the hive, temperature of the environment outside of the hive, the humidity of the environment outside of the hive, and bee activity counter. The sensors used are Strain Gauge Load Cell (SGLC) sensor for weighing purposes, DHT22 sensors for temperature and humidity, and infrared transceivers bee counter sensor for bee activity monitoring. All installed sensors were controlled by using a NodeMCU microcontroller. All data were recorded and transferred to a Google Firebase real-Time database. The proposed system offers an android application to access the recorded data called EMAS apps. EMAS fetches all the information from the database and represents it on graphs and pages in the user smart devices. This paper analyses the data obtained for 36 hours from a single hive. Results obtained represent a relationship between the temperature collected and bee activity with the honey produced. It was observed that in the morning, the increase of temperature leads to high traffic of bees going out of the hive, which decreases the weight of the hive to 2.7 Kg. Meanwhile, in the evening, the decrease in temperature leads to high traffic of bees going into the hive, which increases the hive weight to 4.5 Kg. For future work, to enhance the system's performance, installation of the embedded system into an array of hives was advised and longterm data observation process was required

Study of the colony-environment relationship in domestic bee populations (Apis mellifera L.) by implementing electronic remote monitoring systems

La polinización es la aportación principal de la abeja doméstica (Apis mellifera L.) a los ecosistemas terrestres, y además resulta fundamental para el éxito de muchos cultivos. Sin las abejas podría estar seriamente comprometida la viabilidad de muchas especies vegetales. Sin embargo, las poblaciones de abejas están sufriendo importantes pérdidas, decreciendo debido a diferentes factores no bien identificados, aunque el cambio climático ha sido propuesto como uno de ellos. Por tanto, entender cómo responden las abejas a los nuevos escenarios climáticos es esencial para hacerle frente, especialmente en las zonas bioclimáticas más sensibles, como es el área mediterránea. En este sentido, es necesario conseguir toda la información posible sobre cómo interactúan las abejas con las condiciones ambientales, y cómo son capaces de regular estas condiciones en el interior de la colmena, empleando además métodos lo menos intrusivos posibles, evitando así modificar las condiciones naturales y obtener datos más realistas. Con ese objetivo, hemos diseñado un sistema de monitorización remota, al que hemos denominado WBee, basado en la tecnología Waspmote, y diseñado como un modelo jerárquico a tres niveles: nodo inalámbrico, un servidor local, y un servidor para almacenar los datos en la nube. WBee es un sistema fácilmente adaptable en relación al número y tipo de sensores, al número de colmenas y a su distribución geográfica. WBee además almacena los datos en cada uno de los niveles por si se produjeran errores en la comunicación, disponiendo los nodos también con baterías de apoyo, lo que permite continuar recabando información aunque se produzca una caída del sistema eléctrico. Actualmente el sistema está dotado con sensores que le permiten monitorizar la temperatura y la humedad relativa de la colonia en tres puntos diferentes, así como el peso de la colmena. Todos los datos recogidos se pueden consultar a tiempo real con acceso a través de internet. Una vez implementado el sistema, apoyándonos en los datos obtenidos, hemos estudiado la relación de las abejas con el medio en tres situaciones: en la primera, monitorizamos las tres variables (peso, temperatura y humedad relativa) a lo largo de un mes en 20 colmenas, coincidiendo con una floración comercial de girasol. Esto nos ha permitido entender la evolución de las colonias durante una floración, registrar la producción de miel en las colmenas y estimar el momento óptimo para su extracción, además de verificar el correcto funcionamiento del sistema Wbee. En la segunda, se estudió la influencia de episodios de temperaturas extremas en las colmenas durante el periodo de floración en las campañas apícolas de 2016 y 2017. En este ensayo usamos los cambios en el peso de las colmenas como variable indicadora de la evolución de las colonias, y lo completamos con evaluaciones exhaustivas en tres momentos críticos (principio, mitad y final) de la floración en su conjunto, determinando la población de abejas adultas, cría, y reservas de polen y miel. Los resultados mostraron que la floración se redujo en tres semanas en 2017 en comparación con 2016, ya que las condiciones adversas afectaron significativamente a la evolución normal de las poblaciones de abejas y las reservas de polen y miel, incrementando el estrés alimenticio de las abejas. Esto también afectó al espectro polínico y a las características comerciales de la miel. En la tercera, se registraron los datos de peso, humedad y temperatura de 10 colmenas de abejas ibéricas durante los mismos dos años completos. Estos datos fueron usados para identificar los factores climáticos que potencialmente afectan al comportamiento regulatorio interno en las colmenas y el peso de las mismas. Sobre estos datos se realizó un análisis categórico de los componentes principales (CATPCA) que fue usado para determinar el número mínimo de los factores capaces de explicar el máximo porcentaje de la variabilidad registrada en los datos. A continuación, se usó una regresión categórica (CATREG) para seleccionar los factores que estaban relacionados linealmente con el peso, temperatura y humedad interna de las colmenas, con los que proponer ecuaciones de regresión específicas para abejas ibéricas. Los resultados obtenidos, especialmente aquellos relacionados con la humedad relativa, contrastan con los previamente publicados en otros estudios con abejas en el centro y norte de Europa, y pueden ayudar a planificar una apicultura más eficiente, así como a conocer el efecto del cambio climático en las abejas. Finalmente, los resultados no solo atañen a las abejas, pues el sistema puede ser una herramienta muy útil para estudiar lo que sucede en el medio, usando las colonias de abejas como bioindicadores.Pollination is the main contribution of the domestic bee (Apis mellifera L.) to terrestrial ecosystems, and it is also essential for the success of many crops. Without bees, the viability of many plant species could be seriously compromised. However, bee populations are suffering significant losses, and are decreasing due to different factors not well identified, although climate change has been proposed as one of them. Therefore, understanding how bees respond to new climate scenarios is essential to face it, especially in sensitive bioclimatic zones, such as the Mediterranean area. In this sense, it is necessary to obtain a large amount of information on how bees interact with environmental conditions, and how they are able to regulate these conditions inside the hive, also using the least intrusive methods possible, and avoiding modifying natural conditions and obtaining more realistic data. With this objective, we have designed a remote monitoring system, which we have called WBee, based on Waspmote technology, and designed as a hierarchical model at three levels: wireless node, a local data server, and a cloud data server. WBee is an easily adaptable system in relation to the number and type of sensors, the number of hives and their geographical distribution. WBee saves the data in each of the levels if there are failures in communication, also include a backup battery, which makes it possible to continue collecting data in the event of a power outage. Currently the system is equipped with sensors that allow it to monitor the temperature and relative humidity of the colony at three different points, as well as the weight of the hive. All the data collected can be consulted in real time with Access through the internet. Once the system was implemented, we have studied, based on the data obtained, the relationship of bees with the environment in three situations: in the first, we evaluated the three variables (weight, temperature and relative humidity) over a month in 20 hives, coinciding with a commercial sunflower flowering. This has allowed us to understand the evolution of the colonies during a flowering period, to record the production of honey in the hives and to estimate the optimal moment for its extraction, in addition to verifying the correct functioning of the Wbee system. In the second, the influence of episodes of extreme temperatures in the hives during the flowering period, in the 2016 and 2017 beekeeping sessions, was evaluated. In this study we use the changes in the weight of the hives as a reflection of the evolution of the colonies, and we complete it with exhaustive assessments at three critical moments (beginning, middle and end) of the flowering, determining the population of adult bees, brood, and pollen and honey reserves. The results showed that flowering was reduced by three weeks in 2017 compared to 2016, since the normal evolution of bee populations and pollen and honey reserves were significantly affected by adverse conditions, increasing the nutritional stress of the bees. This also affected the pollen spectrum and the commercial characteristics of honey. In the third, the weight, humidity and temperature data of 10 hives of Iberian bees were recorded during the same two full years. These data were used to identify climatic factors that potentially affect internal regulatory behavior and their weight in hives. On these data, a Categorical principal components analysis (CATPCA) was carried out, which was used to determine the minimum number of factors capable of explaining the maximum percentage of the variability recorded in the data. Next, a categorical regression (CATREG) was used to select the factors that were linearly related to hive internal humidity, temperature and weight to issue predictive regression equations in Iberian bees. The results obtained, especially those related to relative humidity, contrast with those previously published in other studies with bees in central and northern Europe, and can help to plan more efficient beekeeping, as well as to know the effect of climate change on the bees. Finally, the results do not only concern bees, since the system can be a useful tool to study what happens in the environment, using bee colonies as bioindicators