Abstract
One of the causes of mortality in bees is varroosis, a bee disease caused by the Varroa destructor mite. Varroa destructor 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 Varroa destructor. 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 Varroa detection 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 Varroa destructor 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 Varroa destructor 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.
Highlights
In recent years, we have been observing rapid growth of the advancement in technology, including Internet of Things (IoT) solutions
We propose a system built on an IoT monitoring device and single-board computer that can record video streams, process video frames, use a Tensor Processing Unit (TPU) acceleration to perform on-edge analysis with pre-trained Machine Learning models, and send the data to the Cloud for the purpose of storage, further geo-wide analysis, and notifying beekeepers
Since the varroosis rarely occurs in well-performed beekeeping, measuring power consumption in such a state would not give a good view of the possible requirements for energy
Summary
We have been observing rapid growth of the advancement in technology, including Internet of Things (IoT) solutions. The most popular way of detecting the existence of Varroa destructor is regular and timeconsuming bee observation in beehives performed manually by beekeepers Responding to such needs, IoT solutions started to be implemented for this field. Unlike the other presented, entirely relies on edge and cloud processing, applies ML with two dedicated Convolutional Neural Network (CNN) models for bee identification and Varroa destructor detection, and can notify beekeepers directly in case of detected danger. In this manner, it complements the solutions published so far. We discuss the solution and obtained results and compare the results with the results reported in the related works (Section 5)
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