In controlled environment agriculture (CEA), CO2 enrichment can promote photosynthesis while simultaneously reducing evapotranspiration, but the optimal settings vary depending on crop type, growth stage, and microclimate. This study presents a near-field remote sensing framework that fuses RGB image features with environmental variables to predict the CO2 uptake/respiration dynamics of five leafy vegetables grown in a hydroponic culture system and evaluate their impact on resource efficiency under CO2 control. A hybrid deep model incorporating You Only Look Once version 11 (YOLOv11) and a Residual Network with 50 layers (ResNet50) extracts growth-related visual cues and integrates them with tabular features (CO2, temperature, and light conditions) to predict chamber CO2 dynamics. Performance was evaluated by Mean Absolute Error (MAE)/Mean Squared Error (MSE) on withheld data, and the system-level impacts on water use (ET), pumping energy, and relative yield were analyzed using a conventional greenhouse model. The model exhibited high accuracy (MAE = 0.95; MSE = 1.62). Scenario analysis results showed that increasing ambient CO2 concentration from 400 to 1200 ppm reduced modeled water demand by approximately 11%, increased modeled yield by approximately 9%, and resulted in a corresponding reduction in pumping energy per unit area. Unlike conventional single-crop, table-based approaches, this study demonstrates multi-crop generalization and image-environment fusion for CO2 dynamic prediction, establishing proximity sensing as a viable decision-making layer for CEA. While yield/ET results were simulated rather than measured in long-term trials, and leaf area normalization was not available, the proposed framework provides a viable path for data-driven CO2 control in indoor farms by linking image-based monitoring with operational optimization.
Lee et al. (Wed,) studied this question.