“Adaptive Control of Smart Hydroponic Systems Using Reinforcement Learning: A Comparative Study with PID”

Abstract: Accuracy control in hydroponics systems is indispensable for achieving higher crop yield with lower resource utilization. Conventional control strategies like Proportional-Integral-Derivative (PID).controllers, while steadfast, may not possess the requisite adaptability to best suit intricate and dynamic horticultural environments. This work presents a reinforcement learning (RL)-driven control scheme as a smart replacement for PID in controlling major parameters in the GymHydro hydroponic system, namely lighting, watering, and fertilizer supply The RL setting was simulated using continuous action and state spaces, including environmental and plant growth parameters. More sophisticated algorithms like Proximal Policy Optimization (PPO) and Deep Deterministic Policy Gradient (DDPG) were tested in simulation, with reward functions optimized to support plant health, productivity, and system efficiency. During a six-month simulation duration, the RL-regulated system outperformed the PID-regulated baseline with 12.5% more crop yield, a 10–12.5% decrease in water, energy, and nutrient consumption, and a 3% increase in system uptime. All of these gains were significantly different (p ≤ 0.05). These findings demonstrate the capability of reinforcement learning to facilitate adaptive, data-driven optimization in smart agriculture. The suggested method not only enhances productivity but also aids sustainability objectives by minimizing operational costs and resource consumption. Real-world deployment and integration with IoT platforms will be investigated in future work to further improve robustness and scalability.