Enhancing autonomous agriculture control systems in greenhouses for sustainable resource usage using deep learning techniques

Greenhouse climate control is essential for optimizing crop growth while minimizing resource consumption in controlled environment agriculture. This paper proposes a reinforcement learning (RL) based framework for greenhouse climate control, integrating deep learning models to predict both crop growth and resource consumption. The framework enables an RL agent to optimize greenhouse control setpoints dynamically, maximizing crop yield while ensuring sustainable resource usage. The proposed system incorporates a Multi-Layer Perceptron (MLP) model to predict internal greenhouse climate conditions and Long Short-Term Memory (LSTM) models for crop parameter estimation and resource forecasting. Experimental evaluations demonstrate that the proposed TD3 RL-based greenhouse control system achieves higher crop yield growth rates while optimizing resource usage, outperforming conventional greenhouse control strategies with a 24.05% reduction in irrigation.