# Deep Learning Seehive Monitoring System for Early Detection of the Varroa Mite
## 5 Experimental Scenarios and Results
### Scenario II: Detection Algorithm Accuracy Tests  
Scenario II focuses on the precision results obtained from using the generated neural networks on all three devices operating either in online or offline mode. The measurements used are the Mean Detection Accuracy (MDA) from Equation (1), the mean Average  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CNN Models**} & **Load Time** & \begin{tabular}{c} **Testing Detection** \\ **Time** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Time** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Average** \\ **Time per** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **Time** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Memory** \\ **Usage** \\ **(MB)** \\ \end{tabular} \\ \hline MobileNet & 2.614 & 74.78 & 0.47 & 77.4 & 13.4 \\ \hline MobileNet & 1.92 & 59.32 & 0.37 & 61.24 & 13.1 \\ \hline ResNet-50 & 2.45 & 106.77 & 0.831 & 109.23 & 13.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental Scenario I; end node device operating in offline mode (4 core-ARM CPU) detection time and memory usage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CNN Models**} & **Load Time** & \begin{tabular}{c} **Testing Detection** \\ **Time** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Time** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Average** \\ **Time per** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **Time** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Memory** \\ **Usage** \\ **(MB)** \\ \end{tabular} \\ \hline MobileNet & 3.786 & 265.374 & 2.33 & 269.16 & 81.5 \\ \hline MobileNet & 2.037 & 205.216 & 1.808 & 207.25 & 80.2 \\ \hline ResNet-50 & 1.514 & 681.78 & 6.555 & 683.29 & 81.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental Scenario I; cloud server version I (octa-core system) detection time and memory usage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CNN Models**} & **Load Time** & \begin{tabular}{c} **Testing** \\ **Time** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Time** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Detection** \\ **Average** \\ **Time per** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **Time** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Memory** \\ **Usage** \\ **(MB)** \\ \end{tabular} \\ \hline MobileNet & 2.614 & 74.78 & 0.47 & 77.4 & 13.4 \\ \hline MobileNet & 1.92 & 59.32 & 0.37 & 61.24 & 13.1 \\ \hline ResNet-50 & 2.45 & 106.77 & 0.831 & 109.23 & 13.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental Scenario I; cloud server version II (24-core system) detection time and memory usage.  
Precision (mAP) calculated by Pytorch, and the Success Frequency (S.F.) from Equation (3), based on MDA and from Equation (4), based on mAP, as proposed in [58]. The results from the three systems are presented in Table 5.  
Using the metric S.F, the authors can evaluate which of the systems consumes the most energy in the process of detecting bees. Looking at the values in Table 5 and comparing the columns of S.F., it turns out that Cloud Server Version 2 consumes less power for crawling than the other two systems, since the energy needed for crawling is 99% less than the energy required by the End Node Device and 40-60% less than needed by Cloud Server Version 1.  
\[SF=\frac{MDA}{T_{load}+T_{detect}} \tag{3}\]  
\[SF_{mAP}=\frac{mAP}{T_{load}+T_{detect}} \tag{4}\]  
The following scenario describes the results in detecting the Varroa mite, which is the last step in the proposed Varroa detection procedure as illustrated in Figure 2 (detection algorithm's steps and output).