b+WSN: Smart beehive for agriculture, environmental, and honey bee health monitoring — Preliminary results and analysis

F. E. Murphy, M. Magno, P. Whelan, Emanuel Popo Vici
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引用次数: 26

Abstract

In recent years, various 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 insect for a wide range of crops. It is clear that protecting the population of honey bees worldwide, as well as enabling them to maximise their productivity, is an important concern. The work described in this paper utilised heterogeneous wireless sensor networks technologies to gather data unobtrusively from a beehive, describing the conditions and activity of the honey bee colony. A wide range of sensors were deployed for monitoring the multidimensional conditions within a living beehive (including oxygen, carbon dioxide, pollutant levels, temperature, and humidity). Meteorological and environmental conditions outside the hive were also monitored throughout the deployment. The data were then analysed from a biological perspective to provide insights into honey bee behaviour and health. This led to the development of an algorithm for automatically determining the status of the bee colony. Analysis was also undertaken from a meteorological perspective, which led to the development of an algorithm for predicting short term external weather conditions (rain) based on the conditions observed within the hive. The meteorological conditions were seen to have an impact on the data provided by biological sensors (bees) and physical sensors. This can be exploited to improve the accuracy of local weather prediction. Applications of this algorithm include agricultural and environmental monitoring for accurate short term forecasts for the area local to the beehive.
b+WSN:用于农业、环境和蜜蜂健康监测的智能蜂窝-初步结果和分析
近年来,联合国的各种报告都强调了地球上不断增长的人口对食物供应的日益限制。蜜蜂是食物链的重要组成部分,是多种作物最重要的传粉昆虫。很明显,保护全世界的蜜蜂种群,并使它们的生产力最大化,是一个重要的问题。本文中描述的工作利用异构无线传感器网络技术从蜂巢中收集数据,不引人注目地描述蜂群的状况和活动。部署了广泛的传感器来监测活蜂巢内的多维条件(包括氧气、二氧化碳、污染物水平、温度和湿度)。在整个部署过程中,还监测了蜂巢外的气象和环境条件。然后从生物学角度分析这些数据,以深入了解蜜蜂的行为和健康。这导致了一种自动确定蜂群状态的算法的发展。还从气象角度进行了分析,从而开发了一种基于蜂巢内观察到的条件预测短期外部天气条件(降雨)的算法。气象条件被认为对生物传感器(蜜蜂)和物理传感器提供的数据有影响。这可以用来提高当地天气预报的准确性。该算法的应用包括农业和环境监测,对蜂巢附近的区域进行准确的短期预测。
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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