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

在英国，蜜蜂每年为农业贡献超过4.3亿英镑，其中西方蜜蜂（Apis mellifera）提供了高达50%的传粉者生态系统服务。一些人认为蜜蜂是英国第三重要的家养动物，有大约30万个蜂群和4万多养蜂人。不幸的是，近几十年来，蜜蜂受到了许多相互作用的压力的严重影响，包括农业集约化、土地利用变化、极端天气事件和越来越多的病虫害。这个项目的重点是这些疾病中的两个问题：静脉曲张和慢性蜜蜂麻痹。蜜蜂螨（瓦螨破坏者）于20世纪90年代初抵达英国，它的存在导致本地畸形翅膀病毒具有高致病性，这种情况被称为瓦螨病。这是世界范围内蜜蜂损失的最严重原因，控制措施包括使用拟除虫菊酯来控制螨虫的综合虫害管理，尽管螨虫耐药性问题日益严重。慢性蜜蜂麻痹是由慢性蜜蜂麻痹病毒（CBPV）引起的一种成年蜜蜂疾病。这种疾病是最近才出现的，自2007年以来在英国的患病率有所上升。因此，目前尚无针对这种疾病的循证控制措施。养蜂人需要仔细监测蜂群，并定期检测出静脉曲张或慢性蜜蜂麻痹。然而，在通过对螨种群的常规监测或患病工蜂的可见证据容易发现症状之前，这两种疾病很可能在一个群体中已经存在。先前的研究表明，在感染的早期阶段，受感染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发生变化。新的非侵入性蜂群监测技术提供了检测这些行为变化的机会，特别是蜂群内的声学和振动模式。还可以收集与蜂巢温度、湿度、觅食工蜂飞行出口/返回次数以及当地气象条件有关的其他数据。我们计划从英国的研究和商业养蜂场收集一个季节的蜂箱内和外部气象数据。我们还将定期对蜂群的健康状况进行评估，看是否有静脉曲张和慢性蜜蜂麻痹的症状。我们的监测将从被监测的养蜂场产生大量数据流。最大的数据集将是来自殖民地麦克风的声学数据，我们将通过快速傅里叶变换（FFT）将原始数据简化到频域。FFT数据将通过时间序列自回归或机器学习方法进行建模，并将其他蜂巢内和外部监控流作为“元数据”。将比较这两种建模方法，以确定它们区分患病和健康菌落的有效性，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合作伙伴（Agrisound）密切合作，在能源使用、数据流带宽等方面实施最实用、最具成本效益的现场监测系统。这还包括最合适的数据处理管道（集中式或分散式），以增加系统的实用价值。

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.