## 5 New Technologies in Greenhouses
### Computational Modeling and Simulation
Some of the main technologies and approaches used include Computational Fluid Dynamics (CFD), which is widely used to simulate airflow, heat transfer, and \(CO_{2}\) distribution within greenhouses. It allows a detailed analysis of the internal environment in three dimensions, considering complex climatic variables. CFD is used to study natural and mechanical ventilation, optimize greenhouse design, and analyze the impact of different environmental conditions [256]. A study used CFD to analyze the aerodynamic environment of a three-span greenhouse, highlighting the importance of thermal curtains to prevent heat loss in winter [257]. Finite Element Methods (FEM) and Finite Volume Methods (FVM) are both used to solve heat and mass transfer equations, but they differ in the space discretization approach. FVM is often more efficient in terms of computational time and memory usage compared to FEM. Both were compared for simulations of natural ventilation in greenhouses, with FVM showing better results in terms of accuracy and efficiency [256]. A study compared the efficiency of different discretization methods, including FEM, for simulating natural ventilation in greenhouses. FEM was used to model airflow and temperature distribution within a greenhouse, considering the complexity of the structure and ventilation openings. The study showed that although FEM required greater processing power and memory, it provided detailed results on the distribution of climate variables, which are crucial for optimizing greenhouse design and ventilation strategies [256]. Another study used FVM to simulate the internal environment of a semi-enclosed Venlo-type greenhouse equipped with air conditioners. The CFD model developed with FVM was able to predict the distribution of temperature, water vapor, and \(CO_{2}\) inside the greenhouse. The simulation results showed good agreement with experimentally measured data, allowing a detailed analysis of the effectiveness of heating and ventilation systems, as well as the impact of different leaf area densities (LAD) on the greenhouse microclimate [258]. Models that simulate heat and mass transfer are essential for understanding the microclimate inside greenhouses. They use equations that consider sensible and latent heat fluxes, radiation, and plant evapotranspiration. These models help predict the distribution of temperature, humidity, and \(CO_{2}\), allowing the optimization of the position of heating and ventilation systems [258]. The study by [PERSON] et al. [259] illustrates how dynamic heat and mass transfer models can be used to predict and control the indoor environment of greenhouses. The use of thermal screens, together with accurate modeling, can lead to a significant reduction in energy use and optimization of the microclimate, resulting in greater efficiency and sustainability in protected agricultural production. The integration of photovoltaic panels in greenhouses to generate renewable energy while optimizing the internal microclimate through solar tracking systems is another advanced approach. Simulations have shown that shading-free solar tracking can significantly increase power generation and improve global irradiance uniformity within the greenhouse [260]. Modeling based on Artificial Intelligence (AI) techniques such as neural networks and machine learning algorithms are also employed to model and control complex climate variables that are difficult to model with traditional techniques. A recent example of the use of AI techniques for climate modeling and control in greenhouses is the study by [PERSON] et al. [261]. This study proposed an adaptive system based on artificial neural networks integrated with fuzzy logic, known as the Adaptive Neuro-Fuzzy Inference System (ANFIS). The objective was to predict air humidity, air temperature, internal radiation, and \(CO_{2}\) concentration during the growth of tomato seedlings in a greenhouse.