Manual interpretation of colorimetric test strips is prone to substantial variability due to human error and sensitivity to surrounding illumination conditions, resulting in limited diagnostic reliability at the point-of-care (POC) (only 60.7% accuracy in our evaluation). To overcome these issues, we designed an automated paper strip reader (APSR) built on a Raspberry Pi 4B platform. The device integrates a closed chamber with uniform LED illumination, an USB camera, and Python-based image processing algorithms to quantify 15 water quality parameters, including pH, nitrate, chloride, and selected heavy metals. Images are captured under controlled lighting and analyzed through parameter-specific calibration curves, with results displayed via a touchscreen interface. Validation using diverse water samples showed that APSR achieved an overall accuracy of 88.7%, outperforming human interpretation (60.7%). In particular, the APSR provided perfect detection accuracy (100%) for iron and copper, compared to only 63% and 40% by human readers, respectively. Each test requires approximately 5 s, with the device costing less than US$150 to assemble, and offering one-click data export. This cost-effective automated system provides a robust solution for reliable POC testing in low-resource environments, aligning with the World Health Organization (WHO) ASSURED criteria for diagnostic devices.