## 4 Discussion
This is probably due to the RF model which was trained on data from the combination of one, two, and six viewpoints. Classification was performed without considering the number of viewpoints used, which probably produced suboptimal results and might explain the lower performance. Different models should be developed for each number of viewpoints used; however, this requires a significant amount of additional data to avoid training imbalance between the separate models. Adding more viewpoints does not always improve the performance, as shown in the sensitivity analysis (Figure 8). There are some cases where the classification from a single viewpoint is correct and from two viewpoints is wrong. Especially when the maturity of the pepper is close to the decision boundary, adding an viewpoint that contains a few green pixels can result in a different classification. This could be solved by adding more than one additional viewpoint to obtain a better overview of the whole pepper color; however, this will result in higher robot costs. In most cases, adding only single additional viewpoint reveals enough information to improve the maturity classification. The cost of misclassifying a mature pepper as an immature pepper was not distinguished from misclassifying an immature pepper as a mature pepper. It can be argued that classifying a mature pepper into immature is less costly than the other way around, since the pepper can still be harvested at a later pass of the robotic harvester. However, when a pepper is harvested too late and becomes overmature, it will lead to a reduced shelf life and a lower price. Furthermore, a batch of harvested peppers with less variation in maturity can increase the market value [3]. In this work, classification was performed only for maturity classes 2-4. Complete green peppers (class 1, more than 95% green) were not incorporated since they may be neglected in the detection (similar to [PERSON] et al. [44]) and the fruit should stay on the plant. Classification results for class 1 peppers are likely to be comparable with class 4 results (i.e., perfect classification) as both are homogeneously colored. Adding class 1 peppers will therefore lead to an improved overall CA. It is mainly the distinction between class 2 and 3 which is challenging, and hence this was the focus of this work. All classified peppers were healthy. Incorporating detection of peppers with defect spots as an additional class is, in principle, possible; however, a reliable disease detection requires a 360-degree characterization of the pepper. At the time of maturity classification, the pepper is still attached to the plant, which makes full characterization of the pepper not possible. It must be noted that in this work, maturity classification was performed in lab conditions. However, detection of peppers in real greenhouse conditions has been resolved using many different techniques, e.g., [PERSON] and [PERSON] (2018), [PERSON] et al. (2018), [PERSON] et al. (2018), [PERSON] and [PERSON] (2018). Future work should consider occlusion of peppers by leaves and other plant parts in the next-best-viewpoint selection. These occlusions enhance the need for multiple viewpoints and an algorithm for next-best-viewpoint selection. When incorporating the dynamic viewpoint selection method, the average harvesting time increases by 3-21%. Since additional viewpoints also improve the detection of the fruit (2018), a future approach should combine the viewpoints needed for the pepper detection and the maturity classification. This will lower the cycle time, which is essential to make a robotic harvester economically feasible (2018).