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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.

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.

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/.

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.

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.