## 3 Results  
### Dynamic Viewpoint Selection  
For both red and yellow sweet peppers, taking multiple viewpoints improves the performance compared to a single viewpoint by 6% and 5% (Table 3). Peppers with maturity class 4 are classified perfectly for both red and yellow peppers. The distinction between classes 2 and 3 was more challenging. For red peppers, all performance metrics improved when adding one additional viewpoint, resulting in similar performance to when all viewpoints are used. For yellow peppers, the CA and TNR increase while the TPR decreases slightly; maturity classification of yellow peppers when using all viewpoints yielded slightly lower performance. Selecting the next viewpoint using the proposed dynamic viewpoint selection method yields only a small advantage over adding a randomly selected second viewpoint for the red peppers. For yellow peppers, randomly selecting the next-best viewpoint outperformed the proposed method, implying that for yellow peppers, any additional viewpoint is beneficial for maturity classification, not only the viewpoint with most variability in color. The high performance of the upper bound shows the potential of dynamically selecting an additional viewpoint. Respectively, 6% and 7% of the red and yellow peppers were classified wrongly using a single viewpoint and were classified correctly using dynamic viewpoint selection. On the other hand, 0% and 4% of the peppers were classified correctly using one viewpoint and were classified wrongly using the dynamic viewpoint selection method. In general, the CA for red peppers was higher than for yellow peppers. This is likely due to the more subtle color differences for different maturity levels of yellow peppers since color differences between yellow and green are smaller than red and green. An additional viewpoint was acquired for 39% of the red peppers and 62% of the yellow peppers (Table 4). This increased the robot's cycle time by an average of 3.14 and 5.06 s for red and yellow peppers, respectively (3-21% for a harvesting cycle time of 24-94 s [4, 15, 16, 44]), compared to the cycle time for a single viewpoint. Using dynamic viewpoint selection decreases the total costs by 52% and 69% compared to single and all viewpoints, respectively, for red peppers. For yellow peppers, the decrease in cost is 12% and 38%, respectively. The upper bound costs are lower than the costs yielded using the current method for dynamic viewpoint selection, indicating that the method we used to determine the next-best viewpoint can be improved. The dynamic viewpoint selection method selected the same viewpoint as the upper bound in 29% and 18% of the cases. This partially explains the difference in performance with the current method for dynamic viewpoint selection, but in most cases, there is more than a single additional viewpoint that yields a correct classification. In these cases, the \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**Red Peppers**} & \multicolumn{4}{c}{**Yellow Peppers**} \\ \cline{3-8}  & & **CA** & **TPR** & **TNR** & **CA** & **TPR** & **TNR** \\ \hline Single viewpoint & All & 0.907 & 0.944 & 0.833 & 0.800 & 0.892 & 0.611 \\ \hline \multirow{2}{*}{Dynamic viewpoint selection} & 2 & 0.944 & & & 0.778 & & \\  & 3 & 0.947 & & & 0.762 & & \\  & 4 & 1.000 & & & 1.000 & & \\  & All & 0.963 & 0.972 & 0.944 & 0.836 & 0.865 & 0.778 \\ \hline Random viewpoint & All & 0.944 & 0.972 & 0.889 & 0.855 & 0.892 & 0.778 \\ \hline All viewpoints & All & 0.963 & 0.944 & 1.000 & 0.818 & 0.838 & 0.778 \\ \hline Upper bound & All & 0.981 & 1.000 & 0.944 & 0.945 & 1.000 & 0.833 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy (CA), true positive rate (TPR), and true negative rate (TNR) for single viewpoint, dynamic viewpoint selection, random viewpoint selection, all viewpoints, and the upper bound. For dynamic viewpoint selection, the CA per class is also indicated.