Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison

Abstract

The history recorded moon as the most inspiring object in the sky, but it combined with visibility issues to study the phases. This research paper proposes a novel algorithm named Sunfa Ata Zuyan (SAZ), which is meant to extend the shape detection algorithms to aim for lunar phase deceleration and overcome the difficulties encountered by the previous methods to find the moon and determine its phase. The paper sets to investigate two aims. First, propose the add-on algorithm SAZ to determine the lunar phase's data faster. Secondly, evaluate the Raspberry Pi as the main CPU due to its compact size and power as the primary processor based on the idea of a portable designed system. Then to examine the ability of the SAZ algorithm, it's combined with famous algorithms like hue, saturation and value (HSV), Canny, erosion, shape detection, and binarization has been tested on both personal computers (PC) and Raspberry Pi with the same images being compared. The results show that SAZ will help the shape detection algorithm to find the object and disclose the moon phases. Furthermore, the Raspberry Pi, functioning as a CPU, can perform as a hand-to-hand system to determine the lunar phase as a compact portable remote sensing structure.