Data-Driven Reinforcement Learning for Greenhouse Temperature Control

Efficient temperature control in greenhouses is essential for optimal plant growth, especially in arid regions where the harsh environment poses significant challenges to maintaining a stable microclimate. Maintaining the optimum temperature range directly influences healthy plant development and overall agricultural productivity, impacting crop yields and financial outcomes. However, the greenhouse in the present case study fails to maintain the optimum temperature as it operates based on predefined settings, limiting its ability to adapt to dynamic climate conditions. To maintain an ideal temperature range within the greenhouse while dynamically adapting to fluctuating external conditions, this study introduces a control framework using Deep Deterministic Policy Gradient, a model-free deep reinforcement learning algorithm, to optimize temperature control in the closed greenhouse. A deep neural network is trained using historical data collected from the greenhouse to accurately represent the nonlinear behavior of the greenhouse system under varying conditions. The deep deterministic policy gradient algorithm learns optimal control policy by interacting with a simulated greenhouse environment, continuously adapting without needing an explicit system dynamics model. Results from the study demonstrated that, over a five-day simulation period, the deep deterministic policy gradient control system outperformed the existing greenhouse climate system in temperature regulation. It achieved a mean squared error of 0.01�C and a mean absolute error of 0.13�C. Additionally, the deep deterministic policy gradient algorithm demonstrated a significant improvement in energy efficiency, reducing total energy consumption by 6.80% compared to the current system.