## 5 Discussion  
IoT devices that allow for the monitoring of beehives and detection of varroosis are becoming an important example of the use of advanced IT technologies and electronics in the natural sciences. Such devices can help prevent the spread of _Varroa destructor_ between hives and whole apiaries worldwide, which is important not only for bekeeping itself but also indirectly influences the cultivation of plants and the food industry. Our solution, likewise presented in [45; 46], is built on a single-board computer (Raspberry Pi) platform and aims to analyze video streams with bees and detect varroosis. These are the only similarities between the three systems. However, our solution extends the capabilities of the mentioned solutions by including the modules for the _V. destructor_ detection. The method of image processing in [45] includes sending cropped images of bees to a cloud for further analysis (i.e., genus identification and pollen detection), whereas our solution is based on Edge processing, and cloud operations are performed only when the _V. destructor_ mites are detected. This Edge computing-based approach allows limiting the data transmitted to the data center and the storage space occupied. Indus Bees 4.0 [46] is rather a prototype of a system. The author does not provide any results for _V. destructor_ detection yet. However, he mentions Intel Neural Compute Stick (NCS-2) as a potential accelerator. Currently, he provides only the first no-detailed results for the _V. destructor_ counting system, which still uses camera images taken during routine hive checks. The experiment was taken on a prepared clean slider that included 84 mites and used a k-NN  
Figure 12: Average processing time of a single bee picture for different resolutions. classifier. The solution was more focused on the IoT device than on cooperation with a cloud data center. On the other hand, compared with existing solutions for _V. destructor_ detection, our solution complements them through providing IoT devices capable of data acquisition and early analysis, and through a cloud-based data system for geo-wide analyses and training of detection models. In contrast to the solution proposed by [PERSON] et al. [11], who analyzed video samples taken from DB of Research Center of Tropical Apiculture Studies (National University of Costa Rica), we can collect the data with the designed IoT device and detect _V. destructor_ mite on it. Unlike [PERSON] et al. [44], who recorded images of bees and illuminated their body with spectral waves to locate the _V. destructor_ mite with a special passageway (which can be noticed as invasive), our approach is non-invasive since bees are monitored from some distance without any modifications in the beehive construction. Our solution is also comprehensive--it provides image acquisition, on-edge machine learning-based detection, and cloud processing by notifying the user about detected _Varroa destructor_. By using the Cloud platform, it can also be easily used on a wider scale and globally available (although it requires a cloud subscription for keeping the data center alive on the Cloud). We managed to obtain satisfactory results for identifying bees in the video frames and classifying them as infected or healthy ones using only the camera mounted at the beehive entrance. That approach does not affect bees and their bodies being effective at the same time. In terms of the bee and _Varroa destructor_ detection quality, we also obtained promising results. Whereas in [33], [PERSON] et al. used gas sensors and the SVM classifier and achieved the sensitivity (TPR) between 0.67 and 0.75, depending on the category of _V. destructor_ infestation level (low, medium, high), and specificity (TNR) between 0.93 and 0.97, our solution for _V. destructor_ detection reached 0.94 for TPR and 0.92 for TNR. These are significantly better results for TPR and slightly worse for TNR.