Modular sensory hardware and data processing solution for implementation of the precision beekeeping

Q2 Agricultural and Biological Sciences
V. Komašilovs, A. Zacepins, A. Kviesis, S. Fiedler, S. Kirchner
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引用次数: 10

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/.
模块化传感硬件和数据处理解决方案,实现精密养蜂
为了成功实施精准养蜂方法,需要使用各种硬件和软件解决方案进行大量的蜂群数据收集和处理。本文介绍了用于蜂群主要参数(温度、重量和声音)监测的独立无线硬件系统。监测系统是基于树莓派3计算机与连接传感器。电源由太阳能电池板提供,在没有恒定电源的地方可靠运行。为了方便数据管理,提出并开发了基于云的数据仓库(DW),以方便数据的存储和分析。建议的数据仓库是可伸缩和可扩展的,并且可以使用各种数据输入/数据输出接口用于各种其他现成的硬件解决方案。数据仓库的核心旨在提供数据处理的灵活性和多功能性,而核心中的数据流在数据库之间以可控和可靠的方式组织。本文提出了一种将用于蜂群实时监测的硬件与用于数据处理和可视化的云软件连接在一起的方法。将特定的算法和模型集成到系统中,可以帮助养蜂人远程识别蜂群的不同状态,如蜂群、育雏、蜂群死亡等,并通知养蜂人做出适当的决策/行动。这项研究工作在SAMS项目中进行,该项目由欧盟在H2020-ICT-39-2016-2017呼叫中资助。欲了解更多信息,请访问项目网站https://sams-project.eu/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Agronomy research
Agronomy research Agricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
2.10
自引率
0.00%
发文量
0
审稿时长
7 weeks
期刊介绍: Agronomy Research is a peer-reviewed international Journal intended for publication of broad-spectrum original articles, reviews and short communications on actual problems of modern biosystems engineering including crop and animal science, genetics, economics, farm- and production engineering, environmental aspects, agro-ecology, renewable energy and bioenergy etc. in the temperate regions of the world.
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