AI-Driven Smart Lighting with Smart Controls for Energy Conservation and Sustainable Agricultural Greenhouse Management

The increasing need for energy-efficient solutions in agriculture has resulted in the incorporation of AI-driven smart lighting systems in greenhouse management. This work presents a smart lighting system that employs Q-learning, a reinforcement learning method, to enhance energy efficiency while promoting sustainable plant growth in greenhouses. The system modulates illumination intensity according to real-time variables such as plant species, growth phase, time of day, and external environmental conditions. Through continuous interaction with the greenhouse environment, the Q-learning model acquires the ability to make informed decisions that harmonize energy saving with ideal light conditions for plant health. Q-learning, an off-policy technique, enables the system to independently adjust and enhance its lighting schemes by obtaining feedback through incentives for efficient energy use or penalties for inadequate performance. Gradually, the system acquires knowledge about the most efficient measures to reduce energy expenses while maintaining factory production. The proposed method enables dynamic adjustment of greenhouse illumination, resulting in energy efficiency and sustainable agricultural results. This method not only diminishes operating expenses but also promotes sustainable agricultural practices, making it a viable solution for the future of precision farming.