This overview examines the recent advancements in modeling techniques for greenhouse microclimate and evapotranspiration, which are crucial for optimizing agricultural production in controlled environments. Accurate models are essential for understanding the dynamic interactions between environmental variables such as temperature, humidity, light, and soil moisture, and their effects on plant growth and water usage. This review highlights the latest progress in both physical-based and data-driven models, focusing on their applications, benefits, and limitations in greenhouse settings. The integration of advanced technologies, including machine learning, IoT sensors, and climate control systems, has improved the precision and real-time adaptability of microclimate and evapotranspiration models. Additionally, the development of hybrid models combining simulation and empirical data has enhanced predictive accuracy, contributing to better resource management and sustainability. This paper aims to provide an updated perspective on the state-of-the-art modeling approaches, offering valuable insights for researchers and practitioners in the field of greenhouse agriculture.

Greenhouse microclimate, Evapotranspiration modelling, Climate control systems

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