Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning

One of the causes of mortality in bees is varroosis, a bee disease caused by the <i>Varroa destructor</i> mite. <i>Varroa destructor</i> mites may occur suddenly in beehives, spread across them, and impair bee colonies, which finally die. Edge IoT (Internet of Things) devices...

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Autores principales:	Dariusz Mrozek, Rafał Gȯrny, Anna Wachowicz, Bożena Małysiak-Mrozek
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Internet of Things (IoT) <i>Varroa destructor</i> precision beekeeping machine learning cloud image analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/44dc3c191d6245b2965b595b90a75176
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spelling	oai:doaj.org-article:44dc3c191d6245b2965b595b90a751762021-11-25T16:43:48ZEdge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning10.3390/app1122110782076-3417https://doaj.org/article/44dc3c191d6245b2965b595b90a751762021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11078https://doaj.org/toc/2076-3417One of the causes of mortality in bees is varroosis, a bee disease caused by the <i>Varroa destructor</i> mite. <i>Varroa destructor</i> mites may occur suddenly in beehives, spread across them, and impair bee colonies, which finally die. Edge IoT (Internet of Things) devices capable of processing video streams in real-time, such as the one we propose, may allow for the monitoring of beehives for the presence of <i>Varroa destructor</i>. Additionally, centralization of monitoring in the Cloud data center enables the prevention of the spread of this disease and reduces bee mortality through monitoring entire apiaries. Although there are various IoT or non-IoT systems for bee-related issues, such comprehensive and technically advanced solutions for beekeeping and <i>Varroa detection</i> barely exist or perform mite detection after sending the data to the data center. The latter, in turn, increases communication and storage needs, which we try to limit in our approach. In the paper, we show an innovative Edge-based IoT solution for <i>Varroa destructor</i> detection. The solution relies on Tensor Processing Unit (TPU) acceleration for machine learning-based models pre-trained in the hybrid Cloud environment for bee identification and <i>Varroa destructor</i> infection detection. Our experiments were performed in order to investigate the effectiveness and the time performance of both steps, and the study of the impact of the image resolution on the quality of detection and classification processes prove that we can effectively detect the presence of varroosis in beehives in real-time with the use of Edge artificial intelligence invoked for the analysis of video streams.Dariusz MrozekRafał GȯrnyAnna WachowiczBożena Małysiak-MrozekMDPI AGarticleInternet of Things (IoT)<i>Varroa destructor</i>precision beekeepingmachine learningcloudimage analysisTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11078, p 11078 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Internet of Things (IoT) <i>Varroa destructor</i> precision beekeeping machine learning cloud image analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Internet of Things (IoT) <i>Varroa destructor</i> precision beekeeping machine learning cloud image analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Dariusz Mrozek Rafał Gȯrny Anna Wachowicz Bożena Małysiak-Mrozek Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning
description	One of the causes of mortality in bees is varroosis, a bee disease caused by the <i>Varroa destructor</i> mite. <i>Varroa destructor</i> mites may occur suddenly in beehives, spread across them, and impair bee colonies, which finally die. Edge IoT (Internet of Things) devices capable of processing video streams in real-time, such as the one we propose, may allow for the monitoring of beehives for the presence of <i>Varroa destructor</i>. Additionally, centralization of monitoring in the Cloud data center enables the prevention of the spread of this disease and reduces bee mortality through monitoring entire apiaries. Although there are various IoT or non-IoT systems for bee-related issues, such comprehensive and technically advanced solutions for beekeeping and <i>Varroa detection</i> barely exist or perform mite detection after sending the data to the data center. The latter, in turn, increases communication and storage needs, which we try to limit in our approach. In the paper, we show an innovative Edge-based IoT solution for <i>Varroa destructor</i> detection. The solution relies on Tensor Processing Unit (TPU) acceleration for machine learning-based models pre-trained in the hybrid Cloud environment for bee identification and <i>Varroa destructor</i> infection detection. Our experiments were performed in order to investigate the effectiveness and the time performance of both steps, and the study of the impact of the image resolution on the quality of detection and classification processes prove that we can effectively detect the presence of varroosis in beehives in real-time with the use of Edge artificial intelligence invoked for the analysis of video streams.
format	article
author	Dariusz Mrozek Rafał Gȯrny Anna Wachowicz Bożena Małysiak-Mrozek
author_facet	Dariusz Mrozek Rafał Gȯrny Anna Wachowicz Bożena Małysiak-Mrozek
author_sort	Dariusz Mrozek
title	Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning
title_short	Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning
title_full	Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning
title_fullStr	Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning
title_full_unstemmed	Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning
title_sort	edge-based detection of varroosis in beehives with iot devices with embedded and tpu-accelerated machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/44dc3c191d6245b2965b595b90a75176
work_keys_str_mv	AT dariuszmrozek edgebaseddetectionofvarroosisinbeehiveswithiotdeviceswithembeddedandtpuacceleratedmachinelearning AT rafałgorny edgebaseddetectionofvarroosisinbeehiveswithiotdeviceswithembeddedandtpuacceleratedmachinelearning AT annawachowicz edgebaseddetectionofvarroosisinbeehiveswithiotdeviceswithembeddedandtpuacceleratedmachinelearning AT bozenamałysiakmrozek edgebaseddetectionofvarroosisinbeehiveswithiotdeviceswithembeddedandtpuacceleratedmachinelearning
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Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning

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