Background: Automated colorimetric analysis of urine test strips requires precise optical systems for point-of-care diagnostics. Current commercial readers are expensive and bulky, while smartphone-based solutions suffer from variable lighting conditions and inconsistent image quality. Objective: To systematically evaluate and optimize lens selection for a Raspberry Pi HQ camera-based urine test strip reader, determining the optimal focal length for maximizing image quality and field-of-view coverage at minimal working distances. Methods: Five Arducam lenses (6 mm, 8 mm, 12 mm, 16 mm, 25 mm) were evaluated at a fixed 7.5 cm working distance. Performance metrics included field-of-view coverage, image sharpness (using Laplacian variance, Sobel variance, Brenner gradient, Tenengrad, and frequency domain analysis), geometric distortion, and segmentation accuracy using the Segment Anything Model (SAM). Ground truth validation employed pre-marked reference strips with defined reagent pad regions. Results: The 8 mm lens achieved optimal performance with 100% strip coverage, capturing all ten reagent pads while maintaining 99% segmentation accuracy. Sharpness composite score reached 0.794, providing adequate quality for colorimetric analysis. Maximum geometric distortion of 66 pixels represented an acceptable compromise compared to the 6 mm lens (92 pixels distortion, potentially affecting color accuracy) and longer focal lengths (12-25 mm, capturing only 30-70% of strips). Conclusion: The 8 mm focal length provides the optimal balance for compact urine test strip readers, enabling complete multi-parameter analysis in a portable form factor. This optimization establishes the foundation for developing cost-effective automated colorimetric analysis systems suitable for point-of-care and home healthcare applications.