The pore structure of cocopeat-based substrates critically influences their hydraulic properties, directly affecting water use efficiency in soilless cultivation systems. Previous macroscopic modeling approaches infer pore structures indirectly from water retention curves and rely on empirical parameterization of pore geometry and connectivity, overlooking microscale features that directly control fluid pathways and permeability. To address this gap, this study employed micro-CT imaging to reconstruct the three-dimensional pore structures of coarse cocopeat and a fine cocopeat–perlite mixture. Nine regions of interest (ROIs), representing three typical pore types in each substrate, were selected for quantitative pore structure analysis and pore-scale saturated flow simulations. Results show that over 90% of pore diameters in both substrates fall within the 0–400 μm range, and variations in cocopeat particle size and perlite addition significantly affect average pore diameter, porosity, fractal dimension, and tortuosity, thereby influencing permeability and local flow distribution. This study provides new insights into the microscale mechanisms governing water movement in cocopeat-based substrates and reveals key structural factors regulating hydraulic behavior in soilless cultivation systems.

Greenhouse cultivation plays an important role in food security, resource-use efficiency, and sustainable agricultural development. Temperature management is particularly critical because it directly affects crop growth, yield and quality, energy consumption, and environmental control. To clarify the technological pathways, application boundaries, and future directions of intelligent greenhouse temperature management, this review synthesizes recent studies from a closed-loop perspective. A structured narrative approach was adopted to organize the literature around an integrated framework linking multi-source sensing and data governance, predictive modeling, control strategies, system deployment, and feedback-based operation and maintenance. The review first examines the roles of sensors, Internet of Things (IoT) technologies, wireless sensor networks, and multi-source data fusion in reliable greenhouse state perception. It then compares mechanistic, machine-learning, deep-learning, hybrid and physics-guided models in terms of prediction capability, interpretability, generalization, uncertainty, and control compatibility. Control strategies ranging from rule-based and fuzzy control to model predictive control, reinforcement learning, and RL-MPC are further evaluated with respect to constraints, adaptability, energy efficiency, and operational safety. Finally, edge–cloud collaboration, digital twins, safety fallback, and model lifecycle management are discussed as key enablers for translating prediction and control methods into deployable closed-loop systems. Overall, recent advances indicate that high prediction accuracy alone is insufficient for practical greenhouse management; reliable sensing, trustworthy modeling, safe control, system integration, and long-term maintainability must be considered jointly. Future research should therefore move toward crop-oriented, energy-efficient, trustworthy, and practically deployable closed-loop systems for intelligent greenhouse temperature management.