## 1 Introduction  
In recent years, we have been observing rapid growth of the advancement in technology, including Internet of Things (IoT) solutions. These solutions are developed all over the world for almost every area of our lives. Daily use devices, such as smartphones, smartwatches, TVs, household appliances, or cars are obvious examples. For medicine, IoT solutions are used for remote monitoring of a patient's health state and performing complicated medical surgeries [1]. In agriculture [2], these systems are helpful for controlling environmental factors or monitoring animals [3]. On the other hand, people are becoming aware of environmental and climate changes. It is widely known that bees are crucial for the Earth. They are responsible for pollinating trees and plants, including food crops for humanity. Insects pollinate nearly 90% of global food production [4], while two-thirds of them are estimated to be honey bees. Unfortunately, for almost twenty years, the bee population all over the world has been decreasing. This phenomena was identified as Colony Collapse Disorder (CCD) [5]. The reasons have not yet been fully classified; however, among possible factors, scientists often mention wrong bee nutrition [5, 6], weather conditions (including climate changes) [5, 7], pesticides that are used in agriculture more and more often [4, 8, 9], and various organisms (mites, bacteria,viruses etc.) that attack whole apiaries [4; 8; 10; 11; 12]. One of the mites that endangers bees' lives is _Varroa destructor_ (_V. destructor_) [7; 12; 13]. It reaches up to 1.2 mm in length and is red or brown colored [14]. This is why the human eye can easily notice it without any magnification. _Varroa destructor_ attacks both the bees and their larva, can move between workers and eggs inside a beehive, and feeds on hemolymph [13; 15]. Bees infected with this mite live for a shorter time, are affected with wing and body deformations, have navigation problems, and work less efficiently than their healthy counterparts [14]. What is more, _Varroa destructor_ may transfer other viruses, i.e., Deformed Wing Virus (DMV), Kashmir Bee Virus (KBV), or Acute Bee Paralysis Virus (ABPV) [13; 14; 16]. Currently, the most popular way of detecting the existence of _Varroa destructor_ is regular and time-consuming bee observation in beehives performed manually by beekeepers. Responding to such needs, IoT solutions finally started to be also implemented for this field. However, detecting varroosis with IoT requires the development of devices that can constantly monitor beehives and analyze the video data stream in real-time. This necessitates the use of hardware-accelerated IoT devices since sending the video stream to the monitoring data center in many situations is impossible (due to limited access to the Internet). This paper presents a large-scale IoT Edge- and Cloud-based solution for detecting the _Varroa destructor_ mite among bees that enter and leave the beehive. We propose a system built on an IoT monitoring device and single-board computer that can record video streams, process video frames (images), 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. Our solution, presented in detail further in this paper, in contrast with others, is a complex system designed for beekeepers who struggle with _Varroa destructor_ mite. The use of TPU and machine learning (ML) models enables the recognition of possible threats quickly and efficiently and helps save bees from illness and death. Moreover, by performing the video stream processing (including the image analysis) at the Edge, not in the data center, our approach reduces the need for frequent communication with the data center and the storage space required to keep large streams of video captured by the camera-enabled IoT device.