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 VOLUME Vol.05 Issue01 2025 

PAGE NO. 1-3 

 
 
 
 
 

Enhancements in modeling greenhouse microclimate and 

evapotranspiration: an overview of recent progress 
 

Afram Boamah 

Department Of Water Resources Development, School Of Sustainable Development, University Of Environment And Sustainable 

Development, Pmb Somanya, Eastern Region, Ghana 

 

Received: 18 October 2024; Accepted: 20 December 2024; Published: 01 January 2025 

 

Abstract: 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. 

 

Keywords: Greenhouse microclimate, Evapotranspiration modelling, Climate control systems, Machine learning, 
Data-driven models, Physical-based models, Agricultural sustainability, Water management, Environmental 
variables. 

 

Introduction: Greenhouses provide controlled 
environments for plant growth, allowing year-round 
production and protection against adverse weather 
conditions. The microclimate within a greenhouse, 
including factors such as temperature, humidity, and 
airflow, significantly influences plant development, 
yield, and quality. Understanding and accurately 
predicting the greenhouse microclimate is vital for 
optimizing cultivation practices, resource 
management, and ensuring crop success. Similarly, 
estimating evapotranspiration rates within 
greenhouses is crucial for efficient water usage and 
irrigation scheduling. Over the years, various modelling 
techniques have been developed to simulate and 
predict the greenhouse microclimate and 
evapotranspiration accurately. This paper aims to 
provide an overview of recent advances in modelling 
techniques for greenhouse microclimate and 
evapotranspiration, highlighting their advantages, 
limitations, and applications. 

METHOD 

To compile this overview, a comprehensive literature 
review was conducted. Relevant research articles, 
conference papers, and books were reviewed, focusing 
on modelling techniques specifically designed for 
greenhouse microclimate and evapotranspiration. The 
search was performed using various academic 
databases and search engines, using keywords such as 
"greenhouse microclimate modelling," 
"evapotranspiration modelling," "computational fluid 
dynamics in greenhouses," "machine learning for 
greenhouse microclimate," and "empirical models for 
greenhouse evapotranspiration." The selected articles 
were thoroughly analyzed to identify the key modelling 
techniques and their respective applications in 
greenhouse research. Additionally, emerging trends, 
such as the integration of remote sensing data and 
Internet of Things (IoT) technologies, were explored. 
The collected information was then synthesized and 
organized to provide a comprehensive overview of the 
recent advances in modelling techniques for 
greenhouse microclimate and evapotranspiration. 

 



American Journal of Applied Science and Technology 2 https://theusajournals.com/index.php/ajast 

American Journal of Applied Science and Technology (ISSN: 2771-2745) 
 

 

RESULTS 

The review of literature revealed several notable 
advancements in modelling techniques for greenhouse 
microclimate and evapotranspiration. Three main 
approaches emerged: computational fluid dynamics 
(CFD), machine learning algorithms, and empirical 
models. 

CFD models simulate the fluid flow, heat transfer, and 
mass transfer within the greenhouse environment. 
These models provide detailed spatial and temporal 
information, allowing for a comprehensive 
understanding of airflow patterns, temperature 
distribution, and humidity levels. CFD models have 
been widely used to optimize greenhouse designs, 
evaluate ventilation strategies, and investigate the 
impact of various factors on the microclimate. 

Machine learning algorithms, including artificial neural 
networks, support vector machines, and random 
forests, have gained popularity in modelling 
greenhouse microclimate. These techniques have the 
ability to learn complex relationships between input 
variables and output parameters, enabling accurate 
predictions of temperature, humidity, and other 
microclimate variables. 

Machine learning models have demonstrated 
promising results in forecasting and control 
applications, aiding in decision-making processes for 
greenhouse management. 

Empirical models, based on statistical relationships and 
experimental data, offer a simpler and more 
computationally efficient approach to greenhouse 
microclimate modelling. These models derive 
correlations between input variables and output 
parameters, often using regression analysis. Empirical 
models are valuable for quick estimations and can be 
useful when computational resources are limited. 

Additionally, emerging trends in greenhouse modelling 
include the integration of remote sensing data and IoT 
technologies. Remote sensing provides valuable 
information on vegetation indices, canopy 
temperature, and water stress, which can be 
incorporated into models to improve accuracy. IoT 
technologies, such as sensor networks and automated 
control systems, enable real-time data acquisition and 
feedback, facilitating adaptive management strategies. 

DISCUSSION 

Each modelling technique has its advantages and 
limitations. CFD models offer high spatial resolution but 
require significant computational resources and 
expertise to implement. Machine learning models excel 
at capturing complex relationships but may suffer from 
the black-box nature of their predictions. Empirical 

models are computationally efficient but rely heavily 
on the availability and quality of experimental data. 
Understanding these trade-offs is crucial for selecting 
the most appropriate modelling approach based on the 
specific objectives and resources of the greenhouse 
operation. 

Furthermore, the applications of modelling techniques 
in greenhouse research and practical implementation 
are diverse. Modelling can aid in optimizing 
greenhouse designs, improving ventilation strategies, 
and assessing the impact of environmental factors on 
crop performance. It can also support decision-making 
processes related to irrigation scheduling, energy 
management, and pest control. By accurately 
estimating evapotranspiration rates, models contribute 
to efficient water usage and conservation. 

CONCLUSION 

Advances in modelling techniques have significantly 
contributed to our understanding and management of 
greenhouse microclimate and evapotranspiration. 
Computational fluid dynamics, machine learning 
algorithms, and empirical models offer valuable tools 
for simulating, predicting, and optimizing greenhouse 
environments. The integration of remote sensing data 
and IoT technologies further enhances the accuracy 
and real-time capabilities of these models. By 
leveraging these modelling techniques, greenhouse 
operators can make informed decisions, improve 
resource management, and enhance crop productivity 
while minimizing environmental impacts. However, it is 
important to consider the limitations and trade-offs 
associated with each modelling approach, and further 
research is needed to address challenges such as model 
validation and parameter estimation. Overall, 
modelling techniques continue to evolve and play a 
crucial role in the sustainable development of 
greenhouse agriculture. 

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