## 3 Results
### Sensitivity Analysis  
As expected, the number of times it is worthy to acquire an additional viewpoint increases as the pepper price increases (Figure 8). For red peppers, the increasing number of additional viewpoints resulted in an increase in CA until a maximum is reached at the price of EUR 0.08 per pepper. After that, more viewpoints did not yield higher CA. The upper bound showed a similar pattern.  
For yellow peppers, an additional viewpoint is worthy to acquire until the price per pepper increases to EUR 0.08. After that price, only classifications with a low certainty will use the additional viewpoint. When using entropy for selecting the next-best viewpoint, the pepper price does not influence the classification accuracy. However, the CA of the upper bound increases till EUR 0.08 per pepper. At higher pepper prices, an additional viewpoint is not added, and consequently, the CA remains constant.  
Figure 9 shows the influence of robot costs on the average number of times it is worthy to acquire an additional viewpoint and the related dynamic and upper bound CA for red and yellow peppers. As expected, increasing the robot costs decreases the number of times it is worthy to acquire an additional viewpoint, since taking an additional viewpoint becomes more expensive. The CA of the red peppers decreased at high robot costs, but within the range of robot costs, as defined in Section 2.2.3.2, the CA is stable. For the yellow peppers, within the defined realistic cost range, the CA is stable. Since the upper bound is consistently higher than the dynamic viewpoint, adding a viewpoint that reveals the appropriate information will probably contribute to the classification. Even at robot costs  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Red Peppers**} & \multicolumn{4}{c}{**Yellow Peppers**} \\ \cline{2-10}  & & **Single** & **Dynamic** & **All** & **Viewpoints** & **Bound** & **Single** & **Dynamic** & **All** & **Viewpoints** & **Upper** \\  & & & **Selection** & & & **Sleep** & **Viewpoint** & **Selection** & **Viewpoints** & **Bound** \\ \hline Peppers using additional viewpoint(s) (\%) & 0\% & 39\% & 100\% & 39\% & 0\% & 62\% & 100\% & 62\% \\ \hline Average additional cycle time (s) & 0 & 3.14 & 44.08 & 2.75 & 0 & 5.06 & 44.08 & 4.53 \\ \hline \multirow{3}{*}{Costs (EUR)} & Misclassification & EUR 0.98 & EUR 0.39 & EUR 0.39 & EUR 0.20 & EUR 2.22 & EUR 1.82 & EUR 2.02 & EUR 0.61 \\  & Additional sensing & EUR 0.00 & EUR 0.08 & EUR 1.13 & EUR 0.07 & EUR 0.00 & EUR 0.13 & EUR 1.15 & EUR 0.12 \\  & Total & EUR 0.98 & EUR 0.47 & EUR 1.53 & EUR 0.27 & EUR 2.22 & EUR 1.95 & EUR 3.17 & EUR 0.73 \\ \hline Costs per pepper (EUR piece\({}^{-1}\)) & EUR 0.018 & EUR 0.009 & EUR 0.028 & EUR 0.005 & EUR 0.040 & EUR 0.035 & EUR 0.058 & EUR 0.013 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentage of classifications using more than one viewpoint, the additional cycle time, the total associated costs and associated costs per pepper for the single viewpoint, the dynamic viewpoint selection method, all viewpoints, and upper bound.  
Figure 8: Influence of the pepper price on the average number of viewpoints and the classification accuracy for the red (**a**) and yellow (**b**) peppers. The green area indicates the range of sweet pepper market prices of 2019 as mentioned in Section 2.2.3.2.  
of EUR 0.01/s, the highest examined cost, an additional viewpoint is applied for 5% of the peppers. Apparently, the certainty from the first viewpoint of these peppers was really low, and therefore an additional viewpoint was worthy regardless of the robot cost.