Tag Archives: Precision beekeeping

xxx A. Zacepins, N. Ozols, A. Kviesis, J. Gailis, V. Komasilovs, O. Komasilova and V. Zagorska
Evaluation of the honey bee colonies weight gain during the intensive foraging period
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Evaluation of the honey bee colonies weight gain during the intensive foraging period

A. Zacepins¹*, N. Ozols², A. Kviesis¹, J. Gailis², V. Komasilovs¹, O. Komasilova¹ and V. Zagorska²

¹Department of Computer Systems, Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, Liela iela 2, LV-3001 Jelgava, Latvia
²Institute of Plant Protection Research ‘Agrihorts’, Latvia University of Life Sciences and Technologies, Paula Lejina iela 2, LV-3004 Jelgava, Latvia
*Correspondence: aleksejs.zacepins@llu.lv

Abstract:

Beekeeping in Latvia has a long tradition and it is a classical branch of agriculture. In Latvia, there is no traditional beekeeping region, and beekeeping is performed in all regions. Honey yield is influenced by various factors – variety of crops (nectar plants) around the apiary, man-made changes in land/forests (deforestation), climate change, beekeepers’ actions, etc. Application of information and communication technologies (ICT) in the field of beekeeping can bring benefits to the beekeepers. To be more specific, continuous remote monitoring of certain bee colony parameters can improve beekeeper’s apiary management, by informing timely about the nectar flow (or even provide information on bee colony states, e.g., swarming). In such a way, beekeepers can plan their next actions – prepare supers or even choose to move the apiary to a different geographical location. Within this research, weight gain of the ten honey bee colonies was remotely monitored and analysed during two-week period at the beginning of the summer 2021 in Vecauce, Latvia, using the precision beekeeping approach. This monitoring period corresponded to intensive flowering of the winter rapeseed and field beans. Colonies were equipped with the automatic scales. In addition, colony and environmental temperature was monitored. Measurements were taken every thirty minutes. Analysing the obtained data, weight increase can be observed in all colonies, from 17 to 48 kg. As well, based on weight data, swarming event can be identified. Constant monitoring of weight change can also help to identify daily patterns in honey bee activity.

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1350–1358 O. Komasilova, V. Komasilovs, A. Kviesis, N. Bumanis, H. Mellmann and A. Zacepins
Model for the bee apiary location evaluation
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Model for the bee apiary location evaluation

O. Komasilova¹, V. Komasilovs¹, A. Kviesis¹, N. Bumanis¹, H. Mellmann² and A. Zacepins¹*

¹University of Life Sciences and Technologies, Faculty of Information Technologies, Department of Computer Systems, Liela iela 2, LV-3001, Jelgava, Latvia
²Institute of Computer Science, Adaptive Systems, Humboldt University of Berlin, Unter den Linden 6, DE10117 Berlin, Germany
*Correspondence: aleksejs.zacepins@llu.lv

Abstract:

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. This work is conducted within the Horizon 2020 FET project HIVEOPOLIS (Nr.824069 – Futuristic beehives for a smart metropolis).

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509–517 V. Komasilovs, A. Zacepins, A. Kviesis, S. Fiedler and S. Kirchner
Modular sensory hardware and data processing solution for implementation of the precision beekeeping
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Modular sensory hardware and data processing solution for implementation of the precision beekeeping

V. Komasilovs¹, A. Zacepins¹*, A. Kviesis¹, S. Fiedler² and S. Kirchner²

¹Latvia University of Life Sciences and Technologies, Faculty of Information Technologies, Department of Computer Systems, Liela iela 2, LV-3001 Jelgava, Latvia
²University of Kassel, Department of Agricultural and Biosystems Engineering, Mönchebergstraße 19, DE34109 Kassel, Germany
*Correspondence: aleksejs.zacepins@llu.lv

Abstract:

For successful implementation of the Precision Apiculture (Precision Beekeeping) approach, immense amount of bee colony data collection and processing using various hardware and software solutions is needed. This paper presents standalone wireless hardware system for bee colony main parameters monitoring (temperature, weight and sound). Monitoring system is based on Raspberry Pi 3 computer with connected sensors. Power supply is granted by the solar panel for reliable operation in places without constant source for power. For convenient data management cloud based data warehouse (DW) is proposed and developed for ease data storage and analysis. Proposed data warehouse is scalable and extendable and can be used for variety of other ready hardware solutions, using variety of data-in/data-out interfaces. The core of the data warehouse is designed to provide data processing flexibility and versatility, whereas data flow within the core is organized between data vaults in a controllable and reliable way. Our paper presents an approach for linking together hardware for bee colony real-time monitoring with cloud software for data processing and visualisation. Integrating specific algorithms and models to the system will help the beekeepers to remotely identify different states of their colonies, like swarming, brood rearing, death of the colony etc. and inform the beekeepers to make appropriate decisions/actions. This research work is carried out within the SAMS 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/.

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