## 4 Discussion  
Applying dynamic viewpoint selection to maturity classification improves the classification while reducing the economic costs. By taking an additional viewpoint only when profitable, performance is improved. An additional viewpoint is needed only when the first viewpoint yields an uncertain classification. The upper bound results reveal the potential of the viewpoint selection method. However, the currently implemented next-best viewpoint selection method does not reach best performance (the upper bound classification accuracy). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Pepper** \\ **Color** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Pepper** \\ **Class** \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} **Single** \\ **Viewpoint** \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} **Dynamic** \\ **Viewpoint** \\ \end{tabular} } & \multicolumn{3}{c}{\
\begin{tabular}{c} **All** \\ **Viewpoints** \\ \end{tabular} } & \multicolumn{3}{c}{
\begin{tabular}{c} **Upper** \\ **Bound** \\ \end{tabular} } \\ \cline{3-10}  & & **CCF** & **MCA** & **CCF** & **MCA** & **CCF** & **MCA** & **CCF** & **MCA** \\ \hline \multirow{3}{*}{Red} & 2 & 0.42 & 0.71 & 0.57 & 0.86 & 1.00 & 0.86 & 0.86 & 0.86 \\  & 3 & 0.86 & 1.00 & 0.71 & 0.86 & 0.71 & 0.86 & 0.86 & 1.00 \\  & 4 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\  & All & 0.71 & 0.88 & 0.71 & 0.88 & 0.88 & 0.94 & 0.94 \\ \hline \multirow{3}{*}{Yellow} & 2 & 0.57 & 0.57 & 0.43 & 0.71 & 0.71 & 0.86 & 1.00 & 1.00 \\  & 3 & 0.71 & 1.00 & 0.43 & 1.00 & 0.57 & 0.86 & 1.00 & 1.00 \\ \cline{1-1}  & 4 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ \cline{1-1}  & All & 0.71 & 0.83 & 0.52 & 0.88 & 0.71 & 0.88 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Differences in consistent classification factor (CCF) and majority classification accuracy (MCA) for single viewpoint, dynamic viewpoint selection, all viewpoints, and the upper bound. Figure 9: Influence of robot costs on the average number of viewpoints and the classification accuracy for the red (**a**) and yellow (**b**) peppers. The robot costs in the green area are regarded to be realistic, as mentioned in Section 2.2.3.2. The similar performance of the viewpoint selection method and taking a random next viewpoint implies that any additional viewpoint can improve maturity classification and not only the viewpoint with the most color variation. An improved algorithm for viewpoint selection could be to use a machine learning approach, which learns to predict the next-best viewpoint from training data generated from the upper bound [45]. Apart from only color features, geometric features and occlusions should be taken into account. The CA of the red and yellow peppers were slightly higher compared to the results presented for two viewpoints in [PERSON] et al. [31]. This could be explained by different parameters for the RF classifier and the availability of more viewpoints. In addition to using the two additional viewpoints with a different azimuth angle (numbers 1 and 5 in Figure 7), three additional viewpoints (numbers 2, 3, and 4) with a different tilt angle could be selected as additional viewpoint. Maturity classification of red peppers was better than yellow peppers, similar to previous results [13; 31]. An explanation can be that the yellow color is closer to the green color than red is to green in both HSV and RGB color spaces. As a result, the features discriminating between immature and mature peppers are closer for yellow than for red peppers, which makes selecting threshold values that separate the immature and mature classes in the RF model more difficult. This corresponds to previous results revealing the importance of fitting the color space to the fruit variety and applying adaptive algorithms [46]. Adding all six viewpoints to the point cloud does not yield higher classification accuracy of yellow peppers.