Edge computing in IoT-enabled honeybee monitoring for the detection of Varroa destructor

• Autorzy: Wachowicz Anna , Pytlik Jakub , Małysiak-Mrozek Bożena , Tokarz Krzysztof , Mrozek Dariusz

Identyfikatory

DOI

10.34768/amcs-2022-0026

• Języki publikacji: EN

Abstrakty

EN

Among many important functions, bees play a key role in food production. Unfortunately, worldwide bee populations have been decreasing since 2007. One reason for the decrease of adult worker bees is varroosis, a parasitic disease caused by the Varroa destructor (V. destructor) mite. Varroosis can be quickly eliminated from beehives once detected. However, this requires them to be monitored continuously during periods of bee activity to ensure that V. destructor mites are detected before they spread and infest the entire beehive. To this end, the use of Internet of things (IoT) devices can significantly increase detection speed. Comprehensive solutions are required that can cover entire apiaries and prevent the disease from spreading between hives and apiaries. In this paper, we present a solution for global monitoring of apiaries and the detection of V. destructor mites in beehives. Our solution captures and processes video streams from camera-based IoT devices, analyzes those streams using edge computing, and constructs a global collection of cases within the cloud. We have designed an IoT device that monitors bees and detects V. destructor infestation via video stream analysis on a GPU-accelerated Nvidia Jetson Nano. Experimental results show that the detection process can be run in real time while maintaining similar efficacy to alternative approaches.

Słowa kluczowe

EN

Internet of things; IoT; Varroa destructor; precision beekeeping; machine learning; image processing; edge device

PL

Internet rzeczy; IoT; Varroa destructor; pszczelarstwo; uczenie maszynowe; przetwarzanie obrazu; urządzenie brzegowe

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2022
• Tom: Vol. 32, no. 3
• Strony: 355--369
• Opis fizyczny: Bibliogr. 68 poz., rys., tab., wykr.

Twórcy

autor

Wachowicz Anna

• Department of Applied Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Pytlik Jakub

• Department of Applied Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Małysiak-Mrozek Bożena

• Department of Distributed Systems and Informatic Devices, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Tokarz Krzysztof

• Department of Graphics, Computer Vision, and Digital Systems, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Mrozek Dariusz

• Department of Applied Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)