# Deep Learning Seehive Monitoring System for Early Detection of the Varroa Mite
## 6 Conclusions  
This paper presents a novel Varroa mite detection system that uses cameras and deep learning techniques embedded into the beehive for early detection. The authors present both their system and their detection process. The proposed CNN detection algorithm can be incorporated either in the cloud or at the end-node system devices, modifying the system's architecture and operation accordingly. The authors have experimented with using their system's detection process both online (performing detection process as a cloud-service) and offline (end-node device includes the detection algorithm and provides textual detection output over BLE). The experimental results have demonstrated that the detection process over the cloud is at least 3-4 times faster than if the algorithm is incorporated as a service at the end-node embedded device. The online mode also offers the advantage of easily changing the CNN model with a newly trained one. The disadvantage of the online mode includes communication providers' imagery data uploading costs using NBIoT or LTE Cat-M1 transceivers. On the other hand, in offline mode, the mean detection time per image of 104 s and the CNN model load time of 125 s (MobileNet V2 model) at the embedded quad-core ARM MCU is relatively 40\(\times\)-80\(\times\) times more than the time required for online detection. This detection time interval is significantly less energy efficient since the end-node device is battery operated and requires minimum detections to be performed either once per hour or once per day. The authors' detection algorithm experimentation focused on using different pre-trained models (MobileNet V2, MobileNet V3, ResNet-50), setting the MobileNet V2 model \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proposed Solutions** & **Camera Facing Frames** & **Max Detection Precision (\%)** & **Mean Detection Time (ms)** & **Offline Operation** \\ \hline [PERSON] et al. [33] & No & 36 & 270 & No \\ \hline [PERSON] et al. [34] & No & 72 & 2500 & Yes \\ \hline [PERSON] et al. [36] & - & 87 & 0.05 & No \\ \hline Authors’ system & Yes & 86 & 104,560 (Offline), 470 (Online) & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cross comparison of camera-based detection systems for the detection of Varroosisas the most accurate one in the Faster R-CNN algorithm used the authors' trained network. Finally, the authors experimented with their Varroa mite detection process and provided the evaluation of their detection algorithm's accuracy and precision in cases of actual Varroa incidents to 77% and 86% accordingly. The authors set for future work the extensive evaluation of their proposed system towards Varroa mite outbursts and the extension of their algorithms accuracy, minimizing offline detection time and further classification of the severity of the Varroa incidents detected. Conceptualization, S.K. ; methodology, S.K. ; software, G.V. ; validation, G.V. and A.M.; formal analysis, S.K. and G.V. ; investigation, G.V. and A.M.; resources, S.K. ; data curation, G.V. ; writing--original draft preparation, G.V. ; writing--review and editing, S.K. ; visualization, G.V. and A.M.; supervision, S.K. ; project administration, S.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This research has been supported by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1 EDK-2402). The authors declare no conflict of interest. The following abbreviations are used in this manuscript: