Smart Greenhouse Control System using Multi-Objective Adaptive Policy Gradient and Neural Meta Controller

This confluence of escalating energy costs and upsurges in the demand for high-quality crop yields under uncertain environmental conditions makes a strong case for the urgent optimization of greenhouse operations. The most currently used control systems for smart greenhouses are founded upon static or even narrowly focused optimization approaches, which severely limit their adaptability and efficiency in dynamic settings. These methodologies fail to address the challenge of multiple objectives, such as the consumption of energy and crop yield, with unpreparedness for disturbances caused by external influences and computational bottlenecks. This paper brings about a collective framework for three of the most advanced RL methodologies: Multi-Objective Adaptive Policy Gradient with Environmental Feedback, Hierarchical Reinforcement Learning with Temporal Decision Decomposition, and Neural Meta-Controller with Self-Organizing Sub-Networks. MAPGEF utilizes real-time environmental feedback to dynamically adjust policy weights, thus achieving up to a 25% reduction in energy use and an 18% increase in crop yield through accurate environmental tuning. The HRL-TDD utilizes hierarchical RL combined with temporal decomposition in order to handle tough tasks of a greenhouse, thus improving a decision by up to 30%, with energy efficiency improved by 20% and crop yield stabilized with 15%. NMCSOS introduces a neural meta-controller coordinating self-organizing sub-networks, ensuring robust responses to external disturbances with 33% faster recovery, 22% better energy optimization, and 18% reduced yield variability levels. Together, these methodologies address the pressing need for adaptable, scalable, and energy-efficient greenhouse control systems. By integrating multi-objective learning, temporal decomposition, and self-organizing networks, this work significantly advances greenhouse management, promoting sustainable agriculture and resilience in the face of environmental uncertainties.