## 5 New Technologies in Greenhouses
### Computational Modeling and Simulation
The simulations showed that the ANFIS model was able to efficiently predict the greenhouse's internal conditions, helping to maintain an ideal environment for plant growth. Figure 12 illustrates the computational analysis of temperature distribution and airflow patterns in a greenhouse, crucial elements for efficient climate control in protected growing environments. The top image uses a color scale to represent temperature variation, highlighting hotter and colder areas. The lower image shows the behavior of the airflow, which directly influences the circulation of heat and the exchange of gases inside the greenhouse. Understanding these standards allows you to optimize the design and operation of greenhouses to improve plant growth, energy efficiency and the overall sustainability of agricultural practices. Many researchers address the topic of thermal modeling and greenhouse simulation, mainly in the areas of agricultural engineering, environmental sciences and applied physics. [PERSON] et al. [229] addresses the importance of controlled environment agriculture, with a focus on greenhouses, for constant agricultural production throughout the year. It is noteworthy that greenhouses, due to their lightweight construction and often inefficient operation, consume significant amounts of energy, mainly from fossil fuels, resulting in large carbon footprints. The article reviews strategies to improve energy efficiency in greenhouse operations, addressing the optimization of the control of essential environmental parameters and the implementation of advanced energy simulation technologies. The roles of monitoring systems and control algorithms, in addition to energy modeling, are also discussed as key tools for reducing energy consumption and carbon emissions in greenhouses, pointing out potential areas for future research aimed at a more sustainable and efficient operation. [PERSON] et al. [256], discussed the efficiency of two different discretization methods used as computational fluid dynamics (CFD) solvers for simulating natural ventilation in greenhouses, focusing specifically on the Finite Element Method (FEM) and the Finite Volume Method (FVM). They compare the accuracy and computational efficiency of two simulation codes based on FEM and FVM for incompressible two-dimensional turbulent flow in naturally ventilated greenhouses. The study includes comparisons with experimental data in three types of greenhouses to evaluate the effectiveness of these CFD models. He discusses computational capacity, including required computation time and memory storage, finding that FEM required twice as much computation time per cell and step as FVM, with approximately 10 times greater memory storage needs. The article by [PERSON] et al. [248] explores the thermal performance of an evaporatively cooled greenhouse in Qatar, considering three design factors: greenhouse geometry, operational parameters and geographic location. Geometric parameters included fan elevation, roof shape and aspect ratio; operational parameters focused on airflow rate and location factors evaluated incident solar intensity. The study used Computational Fluid Dynamics (CFD) to model a typical greenhouse according to ASHRAE standards, with simulations validated against measured data on internal air temperature, relative humidity and global solar radiation. The results indicated that positioning induction fans at or below crop height and doubling the ventilation rate from 20 Air Changes Per Hour (ACH) to 40 ACH effectively reduced the air temperature inside the greenhouse. The temperature rise induced by solar radiation was mitigated by increased ventilation rates, with the irregular span roof shape resulting in the lowest average indoor temperature. The effects of aspect ratio were negligible for greenhouses with the same floor area and volume. [PERSON] et al.