Search

Hydrogen has the potential to revolutionize the energy industry due to its clean-burning and versatile properties. It is the most abundant element in the universe and can be produced through a variety of methods, including electrolysis. The widespread adoption of hydrogen faces various challenges, including the high cost of production; thus, it is important to optimise the production processes. This research focuses on development of models for hydrogen production optimisation based on various external factors and parameters. Models based on electricity prices are developed and compared between different market situations. To run hydrogen production more effectively, it is required to use renewable energy sources for the production process. Adding the solar power component to the economic evaluation model outcome is more positive. The Monte Carlo tree search (MCTS) algorithm is adapted to effectively control the electrolysis process. MCTS schedule optimization was performed for a 24 h time horizon applying two time-resolution settings—1 h and 15 min. The results demonstrate the potential of the MCTS algorithm for finding good schedules for water electrolyser devices by taking into account variable environmental factors. Whereas the MCTS with a 15 min resolution ensures mathematically better results, it requires more computational power to solve the decision tree.

Information and communication technologies are part of almost any branch of human lives. During the last decade also beekeeping joined the direction of application of IT tools and solutions and precision beekeeping was defined. Still in the beekeeping many operations and observations are completed manually, and there is a potential to switch to the digital realisation. Information technologies can be used in the beekeeping to partly support the beekeepers by implementation of automatic or semi-automatic solutions for bee colony remote monitoring, apiary record making and other actions. The aim of this paper is to make a summary of the usage of information technology tools by the beekeepers in Latvia, summarizing precision beekeeping development status and conclude about its future development potential. To achieve this aim, in cooperation with Latvian Beekeepers Association, a beekeepers survey was conducted. More than 200 beekeepers shared thoughts and opinions about their application of information and communication technologies for monitoring the bee colonies and apiary management. The summary of the survey conducted is described in this study.

Since honey bees are one of the most important actors in the whole world, it is important to follow the life of these insects in order to preserve them from danger, via a range of risk factors such as Colony Collapse Disorder, pesticides, pests etc. Therefore it is important to identify any abnormalities inside the honey bee colony at an early stage, which may be possible using modern technologies e.g. monitoring systems, data processing, and analysis. This research proposes a solution for honey bee colony state identification using temperature data and fuzzy logic. The detection process proposes a Fuzzy Inference System that receives five input parameters and provides an output (defined as “assessment of the colony”) pointing to (for now) three defined possible states – normal, death, and extreme. The rule base for the inference system was defined taking into account the knowledge of field experts, literature research, previous observations and was based only on temperature data and temperature changes inside the hive during different seasons. The proposed system proved to be quite robust, showing an accuracy value of ~98%, 100% precision and specificity, ~97% recall and ~98% F1 score when tested with validation set.

Precision beekeeping, or precision apiculture, focuses on individual beehive remote monitoring using different measurement systems and sensors. Sometimes, there are debates about the necessity for such systems and the real-life benefits of the substitution of bee colony manual inspection by automatic systems. Remote systems offer many advantages, but also have their disadvantages and costs. We evaluated the economic benefits of the remote detection of the bee colonies’ reproductive state of swarming. We propose two economic models for predicting differences in the benefits of catching a swarm depending on its travel distance. Models are tested by comparing the situation in four different countries (Austria, Ethiopia, Indonesia, and Latvia). The economic model is based on financial losses caused by bee colony swarming and considers the effort needed to catch the swarm following a remote swarm detection event. The economic benefit of catching a swarm after a remote precision beekeeping notification is shown to be a function of the distance/time to reach the apiary. The possible technical range is tempting, but we demonstrated that remote sensing is economically limited by the ability to physically reach the apiary and interact in time, or alternatively, inform a person living close by. An advanced economic model additionally includes the swarm catching probability, which decreases based on travel distance/time. Based on exemplary values from the four countries, the economic potential of detecting and informing beekeepers about swarming events is calculated.

Finding a proper location for a bee apiary is a crucial task for beekeepers and especially for travelling beekeepers. Normally beekeepers choose an appropriate apiary location based on their previous experience and sometimes the location may not be optimal for the bee colonies. This can be explained by different flowering periods, variation of resources at the known fields, as well as other factors. In addition it is very challenging to evaluate how many bee colonies should be placed in one geographical location for an optimal nectar foraging process. This research presents a model for finding the number of honey bee colonies needed for the optimal foraging process in the specific location, taking into account several assumptions. Authors propose to take into account potential field productivity, possible chemical contamination, surroundings of the apiary. To run the model, several steps have to be completed, starting from the selection of area of interest, conversion to polygons for further calculations, defining the roads in the selected area. The outcome of the model number of colonies that should be placed is presented to the user. The Python language was used for the model development. The model can be extended to use additional factors and values to increase the precision of the evaluation. In addition, input from users (farmers, agricultural specialists, etc.) about external factors that can affect the number of bee colonies in the apiary can be taken into account. This work is conducted within the Horizon 2020 FET project HIVEOPOLIS (Nr.824069).

Honey bees are the main pollinators of the most agricultural plants on our planet. Foraging behaviour is one of the distinctive behaviours of honey bees and is like a link between the honey bee colony and the ambient environment. It is well known that environmental and meteorological factors can have either positive or negative impact on the foraging activity and the amount of gathered nectar and honey production. This research summarises the effect of the main agrometeorological factors, like temperature, humidity, wind and intensity of precipitation on the foraging activity. Within this research, a model for potential honey bee colony foraging activity based on the agrometeorological factors is presented and described. For the calculations weather forecast is used, either by the manual input or by the automatic uploading using the JSON format. The outcome of the model allows the beekeepers to predict and evaluate the potential amount of nectar to be foraged during the active season or specific period of time.

In a time marked by ecological decay and by the perspective of a severe backlash of this ecosystem decay and climate devastation onto human society, bold moves that employ novel technology to counteract this decline are required. We present a novel concept of employing Artificial Life technology, in the form of cybernetically enhanced bio-hybrid superorganisms as a countermeasure and as a contingency plan. We describe our general conceptual paradigm, consisting of three interacting action plans, namely: (1) Organismic Augmentation; (2) Bio-Hybrid Socialization and (3) Ecosystem Hacking, which together compose a method to create a novel agent for ecosystem stabilization. We demonstrate, through early results from the research project HIVEOPOLIS, a specific way how classic Artificial Life technologies can create such a living, ecologically active and technologically-augmented superorganism that operates outside in the field. These technologies range from cellular automata and biomimetic robots to novel and sustainable biocompatible materials. Aiming at having a real-world impact on the society that relies on our biosphere is an important aspect in Artificial Life research and is fundamental to our methodology to create a physically embodied and useful form of Artificial Life.

The monitoring and predictions of various multi-level states of honeybee colonies are performed using emerging Internet of Things technologies and data processing methods. It is become common to use multiple sensors and devices providing multi-modal data to monitor a single activity. Modern data analysis and data processing procedures include a step of data fusion in order to provide more accurate input data. This, however, requires implementation of machine learning and large data sets, whereas gathering large data sets of real time and observation data is a common problem for small to medium size apiaries. This why there are no real implementation of data fusion method in precision beekeeping field. The aim of this paper was to introduce the concept of data layering, which aims to solve the global precision beekeeping problems without implementation of machine learning. The concept was demonstrated within the scope of foraging optimization problem using three data sets: flowering calendar data, rainfall precipitation data and bee activity data.

Real time, continuous and remote monitoring of the honeybee colonies with application of information and communication technologies (ICT) is becoming increasingly frequent in industry and in a scientific research. Combination of ICT and beekeeping led to the development of the Precision Beekeeping approach. Successful implementation of the Precision Beekeeping system includes development of the bee colony monitoring hardware solution and computer software for data collection and further analysis. This paper describes developed and implemented bee colony monitoring unit for weight and temperature monitoring. Bee colony weight is one of the key metrics of the strength of a colony. Changes in weight can reflect the productivity rate of the colony, as well as its health and state. Developed monitoring system is based on Raspberry Pi Zero W single board computer with several connected sensors for bee colony temperature and environmental parameter monitoring. Weight is measured using single point load cell with possibility to measure weight up to 200kg, which is enough for the beehive measurements. Data transfer from the remote bee colony is provided by the external 3G router. For data storage and analysis cloud-based data warehouse is developed. Collected data is accessible in the web system with user friendly interface for data visualisation and reporting. Within this research scale calibration process is described and accuracy of the weighting is evaluated and possible challenges are discussed. Described monitoring system is developed within the Horizon 2020 project SAMS, which is funded by the European Union within the H2020-ICT-39-2016-2017 call. To find out more visit the project website https://sams-project.eu/.

Honeybees are predominant and ecologically as well as economically important group of pollinators in most geographical regions. As a result of analysing current situation in studies and practices, a conclusion was drawn that beekeeping sector is in decline. The identified reasons for this are land-use intensification, monocropping, pesticide poisoning, colony diseases, parasites and adverse climate. One of the solutions is to find a proper bee colony harvesting location and use luring methods to attract bees to this location. Usually beekeepers choose the apiary location based on their own previous experience and sometimes the position is not optimal for the bees. This can be explained by different flowering periods, variation of resources at the known fields, as well as other factors. This research presents a model for evaluation of possible apiary locations, taking into account resource availability estimation in different surrounding agricultural fields. Authors propose a model for real agricultural field location digitization and evaluation of possible apiary location by fusing information about available field resources. To achieve this, several steps have to be completed, such as selection of fields of interest, converting selection to polygons for further calculations, defining the potential values and coefficients for amount of resources depending on type of crops and season and calculation of harvesting locations. As the outcome of the model, heat map of possible apiary locations are presented to the end-user (beekeeper) in the visual way. Based on the outcome, beekeepers can plan the optimal placement of the apiary and change it in the case of need. The Python language was used for the model development. Model can be extended to use additional factors and values to increase the precision for field resource evaluation. In addition, input from users (farmers, agricultural specialists, etc.) about external factors, that can affect the apiary location can be taken into account.

Successful implementation of the Precision Beekeeping system includes development of the bee colony monitoring hardware and software for data collection and analysis. This paper focuses on development and installation of such system in two developing countries: Ethiopia and Indonesia. Challenges and peculiarities of the mentioned countries are compared and described in regards of the system installation process, beekeeper’s apiary management practices and economic aspects. Bee colony monitoring hardware is based on Raspberry Pi Zero W single board computer as a main controller of the system and connected sensors for temperature, humidity, weight and acoustics. Software part includes cloud-based data warehouse solution with additional models for data analysis and web interface for the remote data monitoring and visualisation. Authors’ approach is presented for linking together hardware components with a cloud software into single solution oriented on user needs in target countries. Main purpose of Precision Beekeeping systems is detecting the actual status of the colony and informing the beekeeper in case if unwanted colony behaviour occurs. Another significant aspect of the research is evaluation of the possibility to transfer knowledge and expertise to the experts in target countries. This work is carried out within the SAMS - Smart Apiculture Management Services project, which is funded by the European Union within the H2020-ICT-39-2016-2017 call. To find out more visit the project website https://sams-project.eu/.

Remote and automatic monitoring of two Apis Cerana bee colonies was conducted in Indonesia to demonstrate precision beekeeping approach in that region. Successful implementation of the precision beekeeping system includes development of the bee colony monitoring hardware and software for data collection, analysis and visualisation. This paper focuses on development and installation of such systems at the private apiary in Indonesia. For bee colony monitoring at the apiary a developed monitoring unit was used, which is based on ESP microchip, and for the data storage SAMS data warehouse was used. The monitoring results showed that the choice of the location of the temperature sensor is important, as the temperature at the hive sides changes synchronously with the outside temperature. Also, feedback from the beekeeper is collected to further improve the system and monitoring process. This research is conducted within the SAMS – Smart Apiculture Management Services project, which is funded by the European Union within the H2020-ICT-39-2016-2017 call and with close collaboration with the local private beekeeper. To find out more, visit the project website https://sams-project.eu/.

Precision Livestock Farming (PLF) approach requires immense amount of datacollection, aggregation and processing, using various hardware and softwaresolutions, and is applied in many farms trying to achieve the most efficient andeffective way of production. Hardware and software compatible systems capable ofachieving this goal are called Farm Management Information Systems (FMIS), andare a necessity for a complete and successful implementation for PrecisionAgriculture (PA) branch approaches. However, most of commercially availableFMIS do not only focus on crop management, but also have limited availability tosmall and average sized farms, in terms of price, supported language and specificfeatures. Simpler FMIS, on the other hand, do not have necessary capabilities tofully support PLF. There are currently very small amount of high grade pig farmoriented FMIS, especially for farms with less than a hundred of sows. Therefore,there exists the need for solutions for managing farms with limited number ofsows. To help address this need, authors proposed and developed architecture forunified data warehouse (DW), which was scalable and extendable cloud based datastorage and processing system with support of individual data analysis. DW hascapabilities to data interexchange and/or be integrated in existing FMIS throughoutvariety of data-in/data-out interfaces, like UIs, unmanned data supplier orconsumer systems. The core of the DW is designed to provide data processingflexibility and versatility, whereas data flow within the core is organized betweendata vaults in a controllable and reliable way.

Precision Beekeeping (or Precision Apiculture) aims to help beekeepers monitor bee colonies remotely and identify different colony states including deviant behaviour. One monitoring target is the remote identification of bee colony swarming since this is one of the factors that can significantly reduce profitability. To identify temperature dynamics and its patterns for swarming detection, ten colonies were constantly monitored for four months from 1 May to 31 August 2015. Nine swarms were observed during experiments. During the warm-up stage, in the last 10–20 min before take-off, a temperature rise by 1.5–3.4 °C from typical range 34–35 °C to range 37–38 °C was registered by a temperature sensor placed above the polyethylene foil covering the upper hive body under the pillow. For all swarming events it was common that a bee colony needs a relatively small amount of time (from 8 to 20 min) to warm up before take-off. It was concluded that a single temperature sensor above the bee nest combined with a proposed decision support algorithm can be used for automatic remote detection of swarming at take-off stage.

The concept of the smart city has been fashionable in the political arena in recent years. Cities are trying to be modern and provide various ICT based services for their citizens. An efficient public transportation service is critical for the citizens, but traffic congestions are still a problem in cities and are one of the main reasons for public transport delays. Therefore, it is important for citizens to know where the needed public transport vehicle is located at the moment, to know if the transport has already passed the stop or not. Authors of this research propose a real-time public transport tracking system using a global positioning system (GPS) technology module to receive the location of the vehicle in a real-time. System is based on the Raspberry Pi 3, which is used to transfer positioning data received from GPS module to the remote database. Based on received data, the location of the bus is visualised in the developed Web system.

Beehive monitoring can provide useful information for people associated with beekeeping to help to manage their honey bee ( Apis mellifera ) colonies. The information obtained from the monitoring process can contain data about beehive's temperature, humidity, weight etc. Such a monitoring system is a practical tool in Precision Beekeeping. Honey bee colonies can be monitored using various system architectures that are different in methods and approaches. Since there are several monitoring system architectures, beekeeper himself should choose the one that suits his needs. To facilitate the beekeeper's choice of the suitable architecture, a selection algorithm was developed. This paper focuses on different automatic monitoring system architectures for real-time beehive temperature monitoring, distinguishing their advantages and disadvantages.

Development of smart city concept is challenging for every city. Smart city approach consists of several components and one of them is smart traffic. Parking is one of the smart traffic components. Due to the rapid growth of vehicles on the streets, finding an available parking space is becoming a big obstacle in modern life. For cities with large number of residents or places where sports or artistic events take place, search for a free parking lot is a major problem and can be a frustrating experience. Parking management system affects and improves people's daily life experience and is an important component of a modern traffic system. Smart parking system allows drivers to obtain real-time parking data, by using Internet of things technologies to gather and process information from parking lots. Thus, intelligent, innovative and efficient ways for parking are needed to coordinate the parking demands efficiently. Authors of this paper share the approach, which is implemented in Jelgava city in Latvia, where two smart parking systems are developed. One system is based on inductive sensors, which can recognize vehicle on each parking lot. Second approach is based on vehicle identification on video stream, which is used to register cars on parking entrance and exit, counting total number of free parking lots, but without indicating which particular lots are free or occupied.

Implementation of precision apiculture methods into practice is highly depended on availability and cost-effectiveness of honey bee monitoring equipment. Expenses of monitoring system installation and maintenance activities also should be minimized. In order to successfully predict different bee colony states like: swarming, determine nectar collection activity and health of honey bees both in active and passive wintering period, minimum hive parameters that should be monitored are temperature and mass. This research presents conceptual design of Internet of Thing approach for the beekeeping. As well honey bee colony temperature monitoring system based on local wireless sensor network together with GSM/GPRS external interface for packet-based communication with remote server on the Internet is developed. Logical topology of the local network is star with one central module. The hive group consists of 10 units. Each hive in the group is equipped with integral temperature sensor chip (±0.4°C accuracy), low power microcontroller and radio frequency (RF) transceiver module. All components are powered by alkaline cell battery, which can provide energy for one year minimum of autonomous operation with 10-minute data logging interval. The central module is also placed on one of the hives and consists of temperature sensor, RF and GSM/GPRS modules, photovoltaic cell array, battery, charge controller and minimal user interface for operational status reporting. The system allows sensor data logging to local storage and periodic sending to a remote server for further detailed analysis in user web or mobile application.

Honey bee wintering in a wintering building (indoors) with controlled microclimate is used in some cold regions to minimize colony losses due to the hard weather conditions. The behavior and possible state of bee colonies in a dark room, isolated from natural environment during winter season, was studied by indirect temperature measurements to analyze the expression of their annual rhythm when it is not affected by ambient temperature, rain, snow, wind, and daylight. Thus, the observed behavior in the wintering building is initiated solely by bee colony internal processes. Experiments were carried out to determine the dynamics of temperature above the upper hive body and weight dynamics of indoors and outdoors wintered honey bee colonies and their brood-rearing performance in spring. We found significantly lower honey consumption-related weight loss of indoor wintered colonies compared with outdoor colonies, while no significant difference in the amount of open or sealed brood was found, suggesting that wintering building saves food and physiological resources without an impact on colony activity in spring. Indoor wintered colonies, with or without thermal insulation, did not have significant differences in food consumption and brood rearing in spring. The thermal behavior and weight dynamics of all experimental groups has changed in the middle of February possibly due to increased brood-rearing activity. Temperature measurement above the upper hive body is a convenient remote monitoring method of wintering process. Predictability of food consumption in a wintering building, with constant temperature, enables wintering without oversupply of wintering honey.

During the honey bee colony's life cycle different colony states can be observed. At certain situations some of the states can negatively impact colony's development (broodless state, swarming) resulting in possible colony's death and increase of beekeepers costs. On the other hand, when honey bee colony is in active brood rearing stage (at the preferable period) it is a sign that the colony is capable of reproduction. By knowing in which state the bee colony are at a specific moment, without opening the hive, beekeeper can improve his apiary management, e.g., timely prepare for further actions. Within the “Application of Information Technologies in Precision Apiculture” (ITAPIC) project, colony monitoring was performed using one temperature sensor per honey bee hive. This gives enough data to examine temperature dynamics and allows to determine the patterns of the given honey bee colony states. Based on these data, it is possible to develop a honey bee colony state identification process. This can be achieved by inspecting the temperature data and developing algorithms for each honey bee colony state or by applying neural networks. Neural networks are widely used for various tasks, including tasks related to classification and data processing. In this paper authors propose a method for honey bee colony state (commencement of brood rearing period and swarming) detection using neural networks with supervised learning.

Future of the traditional beekeeping is to implement smart apiary management and start to use automatic and remote tools for bee colony monitoring together with beehive control mechanisms to improve bee colony productivity. Within the ERA-NET ICT-Agri project “ITAPIC” different bee colony monitoring and control systems together with its combinations were introduced and analysed. This paper presents authors vision for implementation of Precision Beekeeping together with the smart apiary concept. Different parameters of the bee colony can be monitored: temperature, humidity, gas content, sound, vibration etc. Continuous monitoring of some bee colony parameters is very challenging and not user friendly, allowing using it only for research purposes, not for practical implementation by the beekeepers. Precision beekeeping idea is to introduce tools that can be easily implemented into beekeeping practice. This paper describes developed systems and its combinations for successful smart apiary management. Developed systems are based on temperature, sound and video monitoring. Both data transmission types: wired and wireless are applied and compared. As well discussion of automatic beehive heating or/and cooling system implementation into practice is opened. As apiaries usually are placed outside in rural areas, important part of the smart beekeeping is usage of alternative energy for powering all the devices. Most suitable alternative power supply to this moment is usage of solar power with solar panels, which can be mounted on the hive. Together with hardware part it is needed to develop software part for data observation. Software part should be developed as a web system or/and mobile application. Cloud system with decision support functionality and with additional option for informing the beekeepers about changes in the state of the bee colonies could be considered as well.