TOPdesk is a service management software provider in a wide variety of domains and industries. TOPdesk also offers consultancy to their customers that aims to continuously assess and improve the customer’s experience and service efficiency. TOPdesk offers a Mini Health Check (MHC) to their customers in which aconsultant analyzes how efficiently the customer uses their software based on six Key Performance Indicators (KPI). However, the process of creating an MHC report is very time-consuming as it requires performing a lot of manual steps. Also, the norms used for the KPIs provide little meaning as they are arbitrarily chosen and not specific to the customer’s industry. This report aims to improve the current process of performing an MHC. Research has been done on how the MHC is performed, identifying the suitable technologies and learning the currently existing infrastructure that helped us pave the way to create our product. During our project we managed to create a product that automates the MHC. Through user testing we found that this process now takes about two minutes, where the manual process took about two hours. To create more meaningful norms for the KPIs, we also implemented a benchmarking feature. This allows a company to compare the results of their MHC to other TOPdesk customers in the same sector, country or of similar size. We have some recommendations for TOPdesk for the further development of our product. The MHC process could be streamlined in a few ways, most importantly with respect to the process for getting access to customer data. Benchmarking could become even more useful if data can be more easily gathered from more TOPdesk customers.

Strawberries have a short shelf-life time and thus need to be harvested at the right time to reduce waste. To this end, information about quality attributes is useful. Recently, many computer vision methods have been proposed. Most literature analyzes postharvest, which means that strawberries can only be analyzed after harvesting. As a result, these methods cannot be used to find a good timing to harvest. We analyze strawberries preharvest, so that we can analyze until we find a good timing to harvest. We show that predicting ripeness, sweetness, and firmness of strawberries is possible infield. Further, we analyze strawberry size to find a fitting market. Since we analyze size infield, we find two challenges: occlusions and lack of depth information. We perform inpainting to try to recover the original shape. Results are good on artificial occlusions, but varying on real occlusions as it is difficult to adapt to all kinds of occlusions. We use stereo vision and depth estimation to estimate size. Stereo vision improves size estimation slightly.