A Smart Lighting System Control Using Camera Sensors and CNN-Based Deep Learning for Dining Environments

In this study, an Internet of Things (IoT)-based smart lighting control system is proposed in which user involvement is minimized while the most suitable lighting conditions for dining situations are provided. In dining spaces, lighting is regarded as a critical factor not only in the provision of visual brightness but also in the creation of the atmosphere. It has been demonstrated in previous studies that the perceived surface color of foods can be changed according to the light sources, which directly influences freshness perception and appetite. Accordingly, tailoring lighting conditions to individual food types can accentuate the intrinsic colors of ingredients, thereby improving the visual quality of the dining environment and contributing to enhanced user satisfaction. However, manual lighting control during dining may hinder user engagement, as the cognitive and behavioral effort associated with adjusting the lighting is often perceived as inconvenient, which may lead users to refrain from modifying the lighting environment. With the advancement of artificial intelligence (AI), IoT, and smart lighting technologies, new possibilities have been introduced to overcome this limitation. The system is designed to extend beyond simple convenience, and a personalized dining experience tailored to the user’s contextual needs is offered.