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

Honey Bee Colonies Remote Monitoring System

Bees are very important for terrestrial ecosystems and, above all, for the subsistence of many crops, due to their ability to pollinate flowers. Currently, the honey bee populations are decreasing due to colony collapse disorder (CCD). The reasons for CCD are not fully known, and as a result, it is essential to obtain all possible information on the environmental conditions surrounding the beehives. On the other hand, it is important to carry out such information gathering as non-intrusively as possible to avoid modifying the bees’ work conditions and to obtain more reliable data. We designed a wireless-sensor networks meet these requirements. We designed a remote monitoring system (called WBee) based on a hierarchical three-level model formed by the wireless node, a local data server, and a cloud data server. WBee is a low-cost, fully scalable, easily deployable system with regard to the number and types of sensors and the number of hives and their geographical distribution. WBee saves the data in each of the levels if there are failures in communication. In addition, the nodes include a backup battery, which allows for further data acquisition and storage in the event of a power outage. Unlike other systems that monitor a single point of a hive, the system we present monitors and stores the temperature and relative humidity of the beehive in three different spots. Additionally, the hive is continuously weighed on a weighing scale. Real-time weight measurement is an innovation in wireless beehive—monitoring systems. We designed an adaptation board to facilitate the connection of the sensors to the node. Through the Internet, researchers and beekeepers can access the cloud data server to find out the condition of their hives in real time

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

BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices

Precision Beekeeping, a field of Precision Agriculture, is an apiary management strategy based on monitoring honeybee colonies to promote more sustainable resource usage and maximise productivity. The approach related to Precision Beekeeping is based on methodologies to mitigate the stress associated with human intervention in the colonies and the waste of resources. These goals are achieved by supporting the intervention and managing the beekeeper’s timely and appropriate action at the colony’s level. In recent years, the growth of IoT (Internetof-Things) in Precision Agriculture has spurred several proposals to contribute to the paradigm of Precision Beekeeping, built on different technical concepts and with different production costs. This work proposes and describes an information systems architecture concept named BHiveSense, based on IoT and microservices, and different artefacts to demonstrate its concept: (1) a low-cost COTS (Commercial Off-The-Shelf) hive sensing prototype, (2) a REST backend API, (3) a Web application, and (4) a Mobile application. This project delivers a solution for a more integrated and sustainable beekeeping activity. Our approach stresses that by adopting microservices and a REST architecture, it is possible to deal with long-standing problems concerning interoperability, scalability, agility, and maintenance issues, delivering an efficient beehive monitoring system.info:eu-repo/semantics/publishedVersio

Internet of Things (IoT) Application in Meliponiculture

This paper presents an investigation on the environmental factors affecting meliponiculture (the cultivation of stingless bees on a commercial scale for honey production or pollination) using an internet of thing (IOT) application. Manual collection of data for chosen factors can be sporadic and produce variations from incorrect measurement taking; this can cause complications in producing any important interpretation. The feasibility of online monitoring of bee hive based on IoT application has not been properly explored. For a particular bee hive, the amount of departing and arriving bee was estimated by using non-intrusive sensors of infrared transmitter and receiver. A basic temperature-humidity sensor to monitor the temperature and humidity was placed inside the bee hive.  All sensors were integrated with a microcontroller and a Wi-Fi module which sent the data wirelessly to IoT cloud platform where the data was continuously saved, monitored, and can be retrieved anytime. Colonies of Trigona Itama were active throughout the whole period at the experimental site but the daily activity period was intense in the warmer days (over 30 °C) whereas the relative humidity had no significant effect on the flight activities. Intensity of daily flight activity was greatest in December, 2016. Temperature was the most important variable affecting flight

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

A computer vision approach to monitoring the activity and well-being of honeybees

Honeybees, in their role as pollinators, are vital to both agriculture and the wider ecosystem. However, they have experienced a serious decline across much of the world over recent years. Monitoring their well-being, and taking appropriate action if that is in jeopardy, has thus become a matter of great importance. In this paper, we present an approach based on computer vision to monitor bee activity and motion in the vicinity of an entrance/exit to a hive, including identifying and counting the number of bees approaching or leaving the hive in a given image frame or sequence of image frames

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

Low-Cost Inventions and Patents

Inventions have led to the technological advances of mankind. There are inventions of all kinds, some of which have lasted hundreds of years or even longer. Low-cost technologies are expected to be easy to build, have little or no energy consumption, and be easy to maintain and operate. The use of sustainable technologies is essential in order to move towards a greater global coverage of technology, and therefore to improve human quality of life. Low-cost products always respond to a specific need, even if no in-depth analysis of the situation or possible solutions has been carried out. It is a consensus in all industrialized countries that patents have a decisive influence on the organization of the economy, as they are a key element in promoting technological innovation. Patents must aim to promote the technological development of countries, starting from their industrial situations