Color sensing has become an essential technology in computer vision, with significant applications in agriculture, manufacturing, healthcare, and automation. It enables machines to accurately detect, distinguish, and interpret colors, supporting critical tasks such as object tracking, quality control, and informed decision-making. While advanced artificial intelligence tools are increasingly integrated into daily life, color recognition remains a simple, reliable, and efficient solution, especially in controlled environments where precision and speed are paramount. A color model, or color space, provides a structured numerical framework for representing colors, ensuring consistent interpretation across different digital devices and imaging systems. Among the available models is the most commonly used due to its straightforward implementation and broad compatibility. This article reviews widely adopted methods for color space analysis and proposes a novel approach to color interpretation. The proposed method combines low computational complexity, high processing speed, ease of implementation, and robustness to variations in object orientation. These features make it particularly well-suited for onboard vision systems and practical real-world applications, enhancing the efficiency and reliability of color-based image processing (pp.95-102).