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Efficient greenhouse management is essential for sustainable food production, but the high energy demand for climate regulation poses significant economic and environmental challenges. While traditional process-based greenhouse models exist, they are often too complex or imprecise for reliable control. To address this, our study introduces a novel data-driven predictive control framework using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks within a Model Predictive Control (MPC) architecture. Training data were generated from a validated dynamic model simulating lettuce cultivation under various environmental conditions. The LSTM and GRU networks were trained to predict future greenhouse states — including temperature, humidity, CO 2 concentration, and crop dry matter — with robustness confirmed via 10-fold cross-validation. These networks were embedded into an online MPC controller to optimize heating, ventilation, and CO 2 injection, aiming to minimize energy consumption and maximize crop yield while respecting biological constraints. Results showed that both the LSTM- and GRU-based controllers significantly outperformed a conventional MPC baseline. For example, humidity violations dropped from 54.77% (MPC) to 15.45% (GRU) and 17.71% (LSTM), while day–night temperature deviations were kept below 2 ∘ C . The GRU controller further achieved up to 40% lower computation time than its LSTM counterpart, confirming its real-time feasibility. Overall, the proposed GRU-driven predictive control approach offers a robust and computationally efficient solution for intelligent greenhouse climate automation under practical operational constraints. • GRU and LSTM models were applied for greenhouse climate predictive control. • GRU reduced climate violations by 5% compared to LSTM while matching yield. • GRU required 40% less computation time than LSTM in real-time scenarios. • Both models maintained high crop yield and economic performance. • GRU offers a practical solution for energy-efficient greenhouse control.
Published in: Computers and Electronics in Agriculture
Volume 247, pp. 111719-111719