Tillgången till vatten kan ses som en självklarhet men 29\% av världens befolkning saknar tillgång till rent vatten. Vi alla måste ta ett ansvar för att minska vår förbrukning för att spara på den begränsade resurs som vatten faktiskt är. I denna rapport presenteras en lösning för att minska förbrukningen av vatten inom travsporten. Lösningen består av sensorer som mäter fuktigheten i jorden och en algoritm som räknar ut mängden vatten som behövs för att uppnå ett valt värde. För att utvärdera lösningen har en prototyp skapats. Prototypen har genomgått olika tester för att säkerhetställa att den uppfyller de krav och förväntningar som finns. Ett system som kan övervaka banan och få ett exakt värde på mängden vatten som behövs bidrar till en möjlighet att precisionsbevattna. Detta kan bidra till en reducering i vattenanvändningen. Förhoppningsvis så kommer travbanor att kunna dra nytta av denna rapport och hjälpa till med att bidra till en mer hållbar framtid där vi alla strävar efter att minska vår vattenförbrukning. / The access to water could be taken for granted, but 29 % of the world's population lacks access to clean water. We all need to take responsibility for reducing our consumption in order to save on the limited resource.to precision irrigateThis report presents a solution to reduce water consumption in trotting. The solution consists of sensors that measure the moisture level in the soil and an algorithm that calculates the amount of water needed to achieve a selected value. To evaluate the solution, a prototype has been created. The prototype has undergone various tests to ensure that it meets the requirements and expectations that exist. A system that can monitor the course and get an exact value for the amount of water needed contributes to an opportunity to precision irrigate.This could contribute to a reduction in water usage. Hopefully, trotting tracks will be able to benefit from this report and help contribute to a more sustainable future where we all strive to reduce our water consumption.

Harness racing horses are exposed to high workload and consequently, they are at risk of joint injuries and lameness. In recent years, the interest in applications to improve animal welfare has increased and there is a demand for objective assessment methods that can enable early and robust diagnosis of injuries. In this thesis, experiments were conducted on video recordings collected by a helmet camera mounted on the driver of a sulky. The aim was to take the first steps toward equine gait analysis by investigating how semantic segmentation and 3D reconstruction of such data could be performed. Since these were the first experiments made on this data, no expectations of the results existed in advance. Manual pixel-wise annotations were created on a small set of extracted frames and a deep learning model for semantic segmentation was trained to localize the horse, as well as the sulky and reins. The results are promising and could probably be further improved by expanding the annotated dataset and using a larger image resolution. Structure-from-motion using COLMAP was performed to estimate the camera motion in part of a video recording. A method to filter out dynamic objects based on masks created from predicted segmentation maps was investigated and the results showed that the reconstruction was part-wise successful, but struggled when dynamic objects were not filtered out and when the equipage was moving at high speed along a straight stretch. Overall the results are promising, but further development needs to be conducted to ensure robustness and conclude whether data collected by the investigated helmet camera configuration is suitable for equine gait analysis.