Inom Machine Vision är det av stor vikt att kameran har möjlighet att detektera de detaljer som eftersöks. Aliasing är ett problem inom all digital fotografering och beror på att kamerans upplösning är för låg i förhållande till de detaljer den försöker fånga. Det här arbetet analyserar kamerans begränsningar och orsaken till dessa. En enkel kamerarigg som används till försök inom Machine Vision konstrueras om från grunden för bättre kontroll och upplösning och en ny styrning skapas till denna efter beställarens specifikationer. Ett testmönster för ISO 12233:2000 fotograferas därefter i denna rigg. Resultatet analyseras och jämförs mot Nyquists samplingsteorem med avseende på digital fotografering. Resultatet visar hur kamerans konstruktion och sätt att registrera färger genom ett filter framför bildsensorn och algoritmer för att beräkna färgen för varje enskild bildpunkt höjer sampelstorleken med en faktor 3 jämfört med det ursprungliga teoremet om dubbla samplingsfrekven-sen. / Within Machine Vision, it is very important that the camera can detect the details requested. Aliasing is a problem in all digital photography, and is because the camera's resolution is too low relative to the details it tries to capture. This work analyzes the camera's limitations and the cause of these. A simple camera rig used for Machine Vision tests is re-designed for better control and resolution, and a new control-system is created to this according to the client's specifications. A test pattern for ISO 12233: 2000 is then photographed in this rig. The result is analyzed and compared to Nyquist sampling theorem regarding digital photography. The result shows how the camera's design and way of registering colors through a filter in front of the image sensor and algorithms to calculate the color for each individual pixel increases the sample size by a factor of 3 compared with the original theorem with double sampling frequency.

<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>