{"title":"An in-hive soft sensor based on phase space features for Varroa infestation level estimation and treatment need detection","authors":"A. König","doi":"10.5194/jsss-11-29-2022","DOIUrl":null,"url":null,"abstract":"Abstract. Bees are recognized as an indispensable link in the human food chain and general ecological system.\nNumerous threats, from pesticides to parasites, endanger bees, enlarge the burden on hive keepers, and frequently lead to hive collapse.\nThe Varroa destructor mite is a key threat to bee keeping, and the monitoring of hive infestation levels is\nof 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\ncondition 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).\nIn 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\nmetal-oxide gas sensors (in particular, the Sensirion SGP30 and the Bosch Sensortec BME680) was deployed for a substantial part of the 2020\nbee 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.\nThis paper is focused on application-specific invariant feature computation for daily hive activity characterization.\nThe 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.\nFuture work strives to employ a richer sensor palette and evaluation approaches for several hives over a bee season.\n","PeriodicalId":17167,"journal":{"name":"Journal of Sensors and Sensor Systems","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sensors and Sensor Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/jsss-11-29-2022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
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.
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.