# Deep Learning Seehive Monitoring System for Early Detection of the Varroa Mite
## 5 Experimental Scenarios and Results
### System Cross Comparison with Existing Literature Solutions  
Summarizing the existing camera-based technological solutions, as presented in the bibliography, to detect Varroa mite, are presented in Table 7. Table 7 characterizes the proposed system capabilities based on their camera facing setup (inside the brood box facing bee frames or at the beehive door openings), maximum precision recorded, mean detection time per photo, and whether they are capable of online and offline operations. That is, the CNN is implemented in the cloud only and requires end-node devices capable of transmuting images only, or it also supports an embedded CNN implementation, having end-nodes that include the CNN algorithms, and no Internet connectivity is required.  
From Table 7 it is obvious that most of the systems include a camera setup facing the beehive door openings. This is not the case for the early detection of Varroa mite since it grows inside the brood-box frames, making most of the existing propositions incapable of early detection. In addition, only the author's proposed end-node devices and [PERSON]'s [34] may offer offline operation embedded at the end-node devices. [PERSON]'s [33] and [PERSON]'s [36] implementations are mostly cloud-based, with the [PERSON]'s algorithm to also utilize also GPU cores for the detection process.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Class** & **Predicted** & **Predict** & **Accuracy** & **Precision** \\
**Test** & **Varroa** & **No Varroa** & **(\%)** & **(\%)** \\ \hline Actual & TP = 64 & FN = 36 & 77 & 86 \\ Varroa & & & & \\ \hline Actual & FP = 10 & TN = 90 & & \\ No Varroa & & & & \\ \hline \end{tabular}
\end{table}
Table 6: Accuracy and Precision of image processing step on CNN bee-detected contours using HSV color masking and Hough transformation.  
The authors' end-node detection algorithm has similar precision results to the Yolov5 algorithm proposed in [36]. The authors also express serious doubts about the mean detection time of [PERSON]'s CNN implementation, executed in an embedded system that can achieve more than 72% precision. Accuracy measurements are not considered, since most existing systems use the F1 score as an evaluation metric. For that reason, the authors also assume that F1 score values represent mainly precision values of a constant recall value for all system cases examined.  
According to Table 7, the authors' proposed online detection time is about 470 ms. That is two times more than [PERSON]'s solution [33]. Nevertheless, the maximum precision achieved by [PERSON]'s model is 36%, which is 2.3 times less than the precision achieved by our model. Comparing the offline operation of our model and [PERSON]'s [34], the authors also mention that [PERSON]'s implementation is not part of an embedded solution rather than an experimental proof of concept run on a test-bed PC.  
The authors have also preliminarily experimented with the EfficientNet model [59], which is a lightweight model implementation similar to MobileNet V2 and V3 models and has similar accuracy results to the MobileNet V2 model. Nevertheless, loading the EfficientNet trained Neural Network into the system's memory takes 92% more time than the MobileNet V2 model. In addition, the total time required to detect bees using EfficientNet is 20% longer than the time required by MobileNet V2. Taking into account the above excessive model load time, the authors have chosen not to include EfficientNet in this study. The authors set as future work a further reduction of their model offline detection time (which also includes model load time).