Accurate and cost-effective weather prediction is crucial for various industries, yet current methods and tools are either expensive or lack real-time, local applicability. This thesis presents the development and evaluation of a cost-effective sky-imaging weather station designed to accurately track cloud cover using a combination of visual and environmental data. Our research focuses on constructing a system that utilises a single camera and image processing techniques for cloud separation. By employing colour-space filtering and modern image processing methods, we aim to enhance accuracy while minimising costs. The hardware design leverages consumer-grade components, reducing the unit cost to a fraction of existing solutions. The methodology involves an iterative design process, expert consultation, and rigorous testing to refine the prototype. We evaluate the system's performance by comparing sensor readings to METAR data and assessing accuracy. Additionally, we investigate the feasibility of using the Lifted Condensation Level as a substitute for Cloud Base Height. Our findings demonstrate that it is possible to create a sky-imaging weather station at a cost significantly lower than that of comparable products while achieving accurate cloud tracking and separation. This research contributes to the field by offering a practical, low-cost sky imager with potential applications in everyday weather preparedness, industrial forecasting, and solar energy management.