## 6 Conclusions  
In this paper, we presented a comprehensive and efficient solution for detecting and classifying the _Varroa destructor_ mite on bees' bodies. Our approach enables acquiring image data by IoT device, performing machine learning operations related to bee and varroosis detection at the device, transferring the suspicious cases to the data center located in the Cloud, and informing a beekeeper when the infection is detected. The results of our experiments confirm that with the presented approach relying on Edge-Cloud computing hybrid architecture, we can perform the detection of the disease effectively and efficiently. For most of the analyzed video frames, the precision rate (1) reached the value of 1 for bee identification and about 0.7 for the _Varroa destructor_ detection process. For the sensitivity rate (2), most values exceeded 0.7 for bee recognition and 0.9 for the _Varroa destructor_ detection. The F1 score value was similar for both processes and reached around 0.8. Although the effectiveness of the detection processes is still not perfect, we have to remember that the point is to detect _Varroa destructor_ in the beehive as quickly as possible before the disease starts to spread. This is more important than detecting every case of the disease since, after the first notification, the beekeeper can start the healing procedure. In this regard, our solution works sufficiently well. In the future, we can extend our solution in several directions. First of all, we think about improving device powering by adding solar panels. That would make our solution more eco-friendly and limit the frequency of power bank recharging. Another idea can refer to extending a system with bee activity monitoring. Bee behavior can reveal some abnormalities, including various mite infections. For that purpose, using context predictions would be quite an innovative concept regarding bee monitoring. Several context-aware applications, such as those presented in [50] and context-history based systems [51; 52; 53] have proved promising results in other fields (project management--[52], competences management--[50], thus predicting bee condition based on history of their behaviour may bring interesting conclusions. Finally, our further works consider trying other placements of the device to find the best location for obtaining good results, provide easy access for a beekeeper and be non-invasive for bees at the same time. Each approach from the above will bring some new findings and enrich smart beekeeping. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Solution**} & **Varroosis Detection** & **Data Type** & **Algorithm** & **Device** & **Cloud Processing** & **Edge Processing** \\ \hline [PERSON] et al. [45] & Mentioned, not implemented & Video & CNN & Raspberry Pi & Yes & No \\ \hline [PERSON] [46] & Mentioned, not implemented & Video, temperature, humidity, weight & kNN & Raspberry Pi, sensors & No & No \\ \hline [PERSON] et al. [11] & In honeybee cells & Video & Background subtraction & No information & No & No \\ \hline [PERSON] et al. [44] & Yes & Video, Spectral waves & LDA & Passageway, camera, mirror, LED arrays, laptop & No & No \\ \hline [PERSON] et al. [33] & Yes & Gas measurements & SVM & Gas sensors & No & No \\ \hline [PERSON] et al. [34] & Yes & Gas measurements & kNN, LDA & Gas sensors & No & No \\ \hline Our (proposed) solution & Yes & Video & Neural network (AutoML) & RaspberryPi + TPU & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of bee monitoring solutions, including detection of the _Varroa destructor_. **Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON]-[PERSON] designed and implemented the data analysis workflows; [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] corrected the paper. All authors have read and agreed to the published version of the manuscript.