Automated in-hive monitoring and advanced data analytics to detect honey bee diseases

自动蜂巢内监控和先进的数据分析来检测蜜蜂疾病

• 批准号: BB/V017675/1
• 负责人: Giles Budge
• 金额: $ 19.27万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FV017675%2F1
• 关键词: Automated; hive; monitoring; advanced; data

In the UK honey bees contribute over £430m per annum to agriculture, with the Western honey bee (Apis mellifera) providing up to 50% of pollinator ecosystem services. Some suggest the honey bee to be the third most important domesticated animal in the UK, with about 300k colonies and over 40k beekeepers. Unfortunately, honey bees have been badly affected by numerous interacting pressures in recent decades, including agricultural intensification, land use change, extreme weather events, and a growing number of pests and diseases. This project focuses on two of these disease problems: Varroosis and chronic bee paralysis. The honey bee mite (Varroa destructor) arrived in the UK in the early 1990s, and its presence causes the indigenous deformed wing virus to become highly pathogenic, a condition known as Varroosis. This is the most serious cause of honey bee loss worldwide, and control measures include integrated pest management using pyrethroids to control mites, although mite resistance is becoming increasingly problematic. Chronic bee paralysis is a disease of adult bees caused by the chronic bee paralysis virus (CBPV). This disease is more recent, increasing in prevalence since in the UK since 2007. As such, there are currently no evidence-based control measures for this disease.Beekeepers need to monitor colonies carefully and on a regular basis to detect Varroosis or chronic bee paralysis. However, it is highly probable that the two diseases are present within a colony before symptoms can readily be detected through routine monitoring of mite populations or visible evidence of diseased worker bees. Previous research has demonstrated that the behaviours of affected bees starts to change in relatively subtle ways at early stages of infection, and the behaviour of healthy bees towards their diseased sisters also alters. New, non-invasive hive-monitoring technologies provide the opportunity to detect some of these changes in behaviour, in particular the acoustics and vibration patterns within a colony. Additional data related to hive temperature, humidity, foraging worker flight exits/return counts, as well as local meteorological conditions can also be collected.We plan to collect in-hive and external meteorological data from both research and commercial apiaries in the UK across a season. We will also undertake regular assessments of the health of the colonies for Varroosis and symptoms of chronic bee paralysis. Our monitoring will produce large data streams from the apiaries monitored. The largest datasets will be the acoustic data from microphones in the colonies, and we will simplify the raw data to a frequency domain via Fast Fourier Transformation (FFT). The FFT data will then be modelled by either time-series autoregression or machine learning approaches, incorporating other in-hive and external monitor streams as 'meta-data'. The two modelling approaches will be compared, to determine both their effectiveness to discriminate between diseased and healthy colonies, and also their ability to detect disease at an early stage.We will work closely with our industrial partner (Agrisound) to implement the most practical and cost-effective in-field monitoring systems, in terms of energy use, bandwidth for data streaming etc. This will also include the most appropriate data processing pipelines (centralised or decentralised) to increase the practical value of system.

在英国，蜜蜂每年为农业贡献超过4.3亿英镑，西部蜂蜜蜜蜂（Apis Mellifera）提供多达50％的传粉媒介生态系统服务。有些人认为，蜜蜂是英国第三大重要的家居动物，拥有约30万个殖民地和超过40k的养蜂人。不幸的是，最近几十年来，蜜蜂受到了许多相互作用的压力的严重影响，包括农业强化，土地利用变化，极端天气事件以及越来越多的害虫和疾病。该项目重点介绍了其中两个疾病问题：变型和慢性蜜蜂瘫痪。蜜蜂螨（Varroa Destructor）于1990年代初抵达英国，其存在导致土著变形的机翼病毒变得高度致病，这种情况被称为Varroosis。这是全世界造成蜜蜂损失的最严重原因，控制措施包括使用拟除虫菊酯控制螨虫的综合害虫管理，尽管螨虫的耐药性变得越来越有问题。慢性蜜蜂瘫痪是由慢性蜜蜂瘫痪病毒（CBPV）引起的成年蜜蜂疾病。自2007年以来，自2007年以来，这种疾病是最近的疾病，其患病率提高。因此，目前尚无针对这种疾病的基于证据的控制措施。BeeKeepers需要仔细，定期监测菌落以检测变异或慢性蜜蜂瘫痪。但是，在殖民地中出现两种疾病，在症状可以通过常规监测螨虫人群或患病工人的可见证据来检测到症状之前，这两种疾病是有问题的。先前的研究表明，在感染的早期阶段，受影响的蜜蜂的行为开始以相对微妙的方式发生变化，健康蜜蜂对患病姐妹的行为也会改变。新的，无创的Hive-Hive-Hive-Hive Technologies为检测行为中的某些变化，尤其是菌落中的声学和振动模式提供了机会。还可以收集与Hive温度，湿度，觅食工人的航班出口/返回计数以及当地气象条件有关的其他数据。我们计划在整个季节从英国的研究和商业养蜂中收集hive和外部气象数据。我们还将定期评估殖民地的健康状况和慢性蜜蜂瘫痪症状。我们的监控将从受到监视的养蜂场中产生大型数据流。最大的数据集将是菌落中麦克风的声学数据，我们将通过快速傅立叶变换（FFT）将原始数据简化为频域。然后，FFT数据将通过时间序列自动进度或机器学习方法进行建模，将其他内部监视流和外部监视流以“ meta-data”的形式增加。将比较两种建模方法，以确定其区分疾病和健康的殖民地的有效性，以及它们在早期阶段检测疾病的能力。我们将与我们的工业伙伴（Agrisound）紧密合作，以实施最实用和最具成本效益的内部监控系统，以便在能量流中进行综述，以进行数据流程，以进行数据流程，以实现数据流程，以实现数据流，该系统的范围（包括），这些数据的流行情况（complate）的范围（既有数据）。系统的价值。