Sustainable Multi-Zone Greenhouse Fertigation Through Reliability-Aware Sensing, Spatiotemporal Graph Learning, and Multi-Agent Reinforcement Learning

With protected agriculture increasingly developing toward large-scale, multi-zone, and intelligent production, reducing water and fertilizer consumption, drainage losses, and energy waste while maintaining crop yield, product quality, and long-term economic profitability has become an important challenge for sustainable greenhouse production. To address sensor abnormalities, spatial heterogeneity among irrigation zones, competition for shared hydraulic resources, and the limited ability of conventional control strategies to jointly optimize resource efficiency and economic performance, a sensor-driven spatiotemporal graph multi-agent reinforcement learning framework, termed SensorGraph-FertiMARL, is proposed for state sensing, response prediction, and sustainable coordinated fertigation optimization in multi-zone greenhouses. Reliable zone-level states are first obtained through sensor-reliability gating, missing-state reconstruction, and multi-relational graph fusion incorporating spatial, hydraulic, and microclimatic relationships. An action-conditioned fertigation–yield–quality–profit digital twin is then constructed to jointly predict root-zone responses, resource consumption, marketable yield, product quality, and net profit associated with candidate control actions. Finally, graph-based multi-agent proximal policy optimization (Graph-MAPPO) with capacity, safety, and fairness constraints is employed to coordinate fertigation allocation among multiple zones under shared pump, main-pipeline, and nutrient-solution resource constraints. Evaluated through rigorous offline digital-twin simulation and replay testing calibrated against complete multi-stage historical dataset records from an eight-zone commercial greenhouse, experimental results show that the proposed method reduced water and fertilizer use to 75.8% and 78.3% (achieving water and fertilizer saving rates of 24.2% and 21.7%), respectively, relative to the uniform timer-based control baseline, while decreasing the drainage rate to 7.2%. The Grade-A product rate increased to 84.6%, net profit reached 138.9% of the baseline level (a +38.9% net-profit improvement), return on investment (ROI) reached 1.94, and the stress-violation rate was limited to 2.9%. For state sensing and digital-twin prediction, the sensor-state reconstruction mean absolute error (MAE) and coefficient of determination (R2) were 0.036 and 0.918, respectively; the water-content prediction MAE was 0.041, the electrical conductivity (EC) prediction root mean squared error (RMSE) was 0.119, and the drainage prediction mean absolute percentage error (MAPE) was 8.7%. In addition, the R2 for marketable-yield prediction reached 0.912, the macro-averaged F1 (Macro-F1) score for quality-grade prediction reached 0.851, the R2 for net-profit prediction reached 0.904, the mean prediction interval width (MPIW) was 0.142, and the prediction interval coverage probability (PICP) reached 0.931. These results demonstrate that SensorGraph-FertiMARL can simultaneously achieve water and fertilizer conservation, drainage reduction, operational-risk control, and economic improvement while maintaining marketable production and quality, thereby providing an effective intelligent optimization approach for resource-efficient, environmentally sustainable, and economically viable greenhouse production.