An in-hive soft sensor based on phase space features for Varroa infestation level estimation and treatment need detection

IF 0.8 Q4 INSTRUMENTS & INSTRUMENTATION Journal of Sensors and Sensor Systems Pub Date : 2022-01-20 DOI:10.5194/jsss-11-29-2022
A. König
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引用次数: 1

Abstract

Abstract. Bees are recognized as an indispensable link in the human food chain and general ecological system. Numerous threats, from pesticides to parasites, endanger bees, enlarge the burden on hive keepers, and frequently lead to hive collapse. The Varroa destructor mite is a key threat to bee keeping, and the monitoring of hive infestation levels is of major concern for effective treatment. Continuous and unobtrusive monitoring of hive infestation levels along with other vital bee hive parameters is coveted, although there is currently no explicit sensor for this task. This problem is strikingly similar to issues such as condition monitoring or Industry 4.0 tasks, and sensors and machine learning bear the promise of viable solutions (e.g., creating a soft sensor for the task). In the context of our IndusBee4.0 project, following a bottom-up approach, a modular in-hive gas sensing system, denoted as BeE-Nose, based on common metal-oxide gas sensors (in particular, the Sensirion SGP30 and the Bosch Sensortec BME680) was deployed for a substantial part of the 2020 bee season in a single colony for a single measurement campaign. The ground truth of the Varroa population size was determined by repeated conventional method application. This paper is focused on application-specific invariant feature computation for daily hive activity characterization. The results of both gas sensors for Varroa infestation level estimation (VILE) and automated treatment need detection (ATND), as a thresholded or two-class interpretation of VILE, in the order of up to 95 % are presented. Future work strives to employ a richer sensor palette and evaluation approaches for several hives over a bee season.
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一种基于相空间特征的蜂房内软传感器用于瓦罗华侵扰程度估计和治疗需求检测
摘要蜜蜂被认为是人类食物链和整个生态系统中不可或缺的一环。从杀虫剂到寄生虫,许多威胁都会危及蜜蜂,增加养蜂人的负担,并经常导致蜂巢倒塌。瓦螨是养蜂的主要威胁,监测蜂箱侵扰程度是有效治疗的主要问题。尽管目前还没有明确的传感器来完成这项任务,但对蜂箱侵扰程度以及其他重要的蜂箱参数进行持续而不引人注目的监测是令人垂涎的。这个问题与条件监测或工业4.0任务等问题惊人地相似,传感器和机器学习有望提供可行的解决方案(例如,为任务创建软传感器)。在我们的Indus Bee4.0项目中,遵循自下而上的方法,基于常见金属氧化物气体传感器(特别是Sensionon SGP30和Bosch Sensortec BME680)的模块化蜂箱内气体传感系统(称为BeE Nose)在2020年蜜蜂季节的大部分时间里部署在一个蜂群中,用于一次测量活动。Varroa种群规模的基本事实是通过重复常规方法应用来确定的。本文的重点是应用特定的不变特征计算来表征日常蜂箱活动。Varroa侵扰水平估计(VILE)和自动治疗需求检测(ATND)的气体传感器的结果,作为VILE的阈值或两类解释,最高可达95 % 呈现。未来的工作将努力在一个蜜蜂季节为几个蜂箱采用更丰富的传感器调色板和评估方法。
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来源期刊
Journal of Sensors and Sensor Systems
Journal of Sensors and Sensor Systems INSTRUMENTS & INSTRUMENTATION-
CiteScore
2.30
自引率
10.00%
发文量
26
审稿时长
23 weeks
期刊介绍: Journal of Sensors and Sensor Systems (JSSS) is an international open-access journal dedicated to science, application, and advancement of sensors and sensors as part of measurement systems. The emphasis is on sensor principles and phenomena, measuring systems, sensor technologies, and applications. The goal of JSSS is to provide a platform for scientists and professionals in academia – as well as for developers, engineers, and users – to discuss new developments and advancements in sensors and sensor systems.
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