Data-driven model predictive control for irrigation management in agricultural greenhouses under CO2 enrichment

The quest for sustainable agricultural practices has significantly increased, particularly in hyper-arid regions characterised by severe water scarcity, and where ensuring food security is a critical concern. This growing interest is driven by the need for agricultural self-sufficiency in the face of geopolitical uncertainties, population growth, climate change, and the need to reduce the dependency on food imports. This work delves into innovative data-driven strategies tailored for agriculture in such challenging environments, with a specific focus on harnessing the potential of CO 2 enrichment in enhancing resource efficiency by promoting plant growth and optimizing input use in closed greenhouse systems. In this study, a data-driven model predictive control (MPC) is employed within a greenhouse environment to optimise irrigation scheduling. The key focus is utilising the power of the extreme gradient boosting (XGBoost) model to predict dynamic transpiration rates, considering the intricate interplay of microclimate conditions and physiological variations with transpiration. The XGBoost model is configured to incorporate microclimate data encompassing solar radiation, inside temperature, inside relative humidity, and inside CO 2 concentration, along with vegetation indices derived from hyperspectral imaging measurements including NDVI, WBI, and PRI, serving as predictive variables. The model demonstrated a high predictive accuracy, achieving an R 2 of 97.1% and an RMSE of 0.417 mmol/m 2 /s for transpiration estimation. The XGBoost model is then incorporated into the MPC framework to control irrigation while maintaining optimal soil moisture levels. This integration is then used to manage irrigation strategies under two distinct CO 2 enrichment regimes: 400 ppm and 1000 ppm. Findings of this study highlight that the MPC-based irrigation control results in substantial water savings of 42.2% over the course of one week of projections compared to the existing irrigation schedule under varying CO 2 concentrations. Furthermore, when applying the MPC model under different CO 2 enrichment regimes, results reveal a 34% reduction with CO 2 enrichment at 1000 ppm relative to 400 ppm. This research underscores the potential of MPC in closed greenhouse environments, emphasising the advantages of advanced predictive modeling, data integration, as well as continuous rolling optimisation, for achieving optimal irrigation control. It also highlights the capacity of CO 2 enrichment in closed agricultural greenhouses, particularly in regions under conditions of high solar radiation, as an effective practice for reducing water consumption.