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American Journal Of Biomedical Science & Pharmaceutical Innovation    
(ISSN – 2771-2753) 
VOLUME 03 ISSUE 07 PAGES: 1-4 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 6.534) 
OCLC – 1121105677     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Publisher: Oscar Publishing Services 

Servi 

 

 

 

 

 

 

 

 

ABSTRACT 

Greenhouse microclimate plays a crucial role in the growth and productivity of plants, and accurate modelling of this 

environment is essential for optimizing greenhouse operations. Additionally, understanding and accurately estimating 

evapotranspiration rates within greenhouses are critical for effective water management strategies. This paper 

provides an overview of recent advances in modelling techniques for greenhouse microclimate and 

evapotranspiration. Various modelling approaches, including computational fluid dynamics (CFD), machine learning 

algorithms, and empirical models, are discussed. The advantages and limitations of each technique are highlighted, 

along with their applications in greenhouse research and practical implementation. Furthermore, emerging trends in 

modelling, such as the integration of remote sensing data and Internet of Things (IoT) technologies, are explored. The 

paper concludes with a discussion on the future directions and potential challenges in modelling greenhouse 

microclimate and evapotranspiration. 

KEYWORDS 

Greenhouse microclimate, evapotranspiration, modelling techniques, computational fluid dynamics, machine 

learning, empirical models, remote sensing, Internet of Things, optimization, water management. 

INTRODUCTION 

  Research Article 

 

ADVANCES IN MODELLING TECHNIQUES FOR GREENHOUSE 

MICROCLIMATE AND EVAPOTRANSPIRATION: AN OVERVIEW 
 

Submission Date: June 21, 2023, Accepted Date:  June 26, 2023,  

Published Date: July 01, 2023  

Crossref doi: https://doi.org/10.37547/ajbspi/Volume03Issue07-01 

 

 

Haofang Dark 
Department of Water Resources Development, School of Sustainable Development, University of Environment 

and Sustainable Development, Pmb Somanya, Eastern Region, Ghana 

Journal Website: 

https://theusajournals.

com/index.php/ajbspi 

Copyright: Original 

content from this work 

may be used under the 

terms of the creative 

commons attributes 

4.0 licence. 

 

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Volume 03 Issue 07-2023 2 

                 

 
 

   
  
 

American Journal Of Biomedical Science & Pharmaceutical Innovation    
(ISSN – 2771-2753) 
VOLUME 03 ISSUE 07 PAGES: 1-4 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 6.534) 
OCLC – 1121105677     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Publisher: Oscar Publishing Services 

Servi 

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. 

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 

https://doi.org/10.37547/ajbspi/Volume03Issue03-01
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Volume 03 Issue 07-2023 3 

                 

 
 

   
  
 

American Journal Of Biomedical Science & Pharmaceutical Innovation    
(ISSN – 2771-2753) 
VOLUME 03 ISSUE 07 PAGES: 1-4 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 6.534) 
OCLC – 1121105677     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Publisher: Oscar Publishing Services 

Servi 

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 

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Volume 03 Issue 07-2023 4 

                 

 
 

   
  
 

American Journal Of Biomedical Science & Pharmaceutical Innovation    
(ISSN – 2771-2753) 
VOLUME 03 ISSUE 07 PAGES: 1-4 

SJIF IMPACT FACTOR (2021: 5. 705) (2022: 5. 705) (2023: 6.534) 
OCLC – 1121105677     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Publisher: Oscar Publishing Services 

Servi 

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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