Neural Network Model for Greenhouse Microclimate Predictions

Petrakis, Theodoros; Kavga, Angeliki; Thomopoulos, Vasileios; Argiriou, Athanassios A.

doi:10.3390/agriculture12060780

Cited by 24 publications

(15 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, Gorzelany et al, 2022 [33] studied the selected mechanical properties of fresh and stored large fruit cranberry fruit using an ANN and MLR. An example of a multilayer perceptron neural network (MLP-NN) designed and applied to model the internal temperature and relative humidity in a greenhouse was included in work by [34]. For the specific NN backpropagation as a training algorithm, the input variables were the external temperature and relative humidity, wind speed, solar irradiance, as well as the internal temperature and relative humidity, up to three timesteps before the modelled time step.…”

Section: Nonlinear Layered Network Trained With Supervision As a Subc...mentioning

confidence: 99%

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

et al. 2023

View full text Add to dashboard Cite

Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific “substitutes” for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments.

show abstract

Section: Nonlinear Layered Network Trained With Supervision As a Subc...mentioning

confidence: 99%

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The model exhibited high accuracy and demonstrated its ability to predict future temperatures, achieving an RMSE value of 0.7. Similarly, Petrakis and Kavga [13], implemented neural network models to forecast microclimates in greenhouses located in Greece. The results indicated maximum errors of 0.88 K and 2.84% for modeled temperature and relative humidity, respectively, while the coefficients of determination were both 0.99 for these parameters.…”

Section: Selection Of the Best Perform Modelsmentioning

confidence: 99%

“…On the other hand, growers' awareness of the upcoming conditions during the day can lead to quicker reactions and better management of energy resources in the greenhouse [9]. Therefore, many studies have been conducted since the early 20th century to model the greenhouse energy loads [10,11], as well as indoor parameters such as temperature [12], humidity [13], light intensity [14], CO 2 [15], etc. The basis for all these research studies is the initial modeling of the greenhouse conditions based on external variables such as temperature, humidity, wind speed, radiation level, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial Intelligence (AI) has become increasingly popular in agricultural studies due to its ability to model complex variables, which is essential in accurately predicting greenhouse climatic parameters and loads. The accurate prediction of microclimate parameters like temperature, humidity, and light intensity plays a crucial role in optimizing crop yield and quality while minimizing energy consumption and environmental impact [3,13,19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Greenhouse Indoor Air Temperature Using Artificial Intelligence (AI) Combined with Sensitivity Analysis

Hosseini Monjezi,

Taki,

Abdanan Mehdizadeh

et al. 2023

Horticulturae

View full text Add to dashboard Cite

Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions are critical for controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) and lighting systems to optimize plant growth and reduce financial losses. In this study, several machine models were employed to predict indoor air temperature in an even-span Mediterranean greenhouse. Radial Basis Function (RBF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) were applied using external parameters such as outside air, relative humidity, wind speed, and solar radiation. The results showed that an RBF model with the LM learning algorithm outperformed the SVM and GPR models. The RBF model had high accuracy and reliability with an RMSE of 0.82 °C, MAPE of 1.21%, TSSE of 474.07 °C, and EF of 1.00. Accurate temperature prediction can help farmers manage their crops and resources efficiently and reduce energy inefficiencies and lower yields. The integration of the RBF model into greenhouse control systems can lead to significant energy savings and cost reductions.

show abstract

“…Historically, these structures have transitioned from simple protective enclosures to complex systems, meticulously managing internal climates to enhance plant growth and extend growing seasons [6]. Contemporary greenhouse operations are increasingly reliant on innovative methods to improve energy efficiency and microclimate control.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

Ećim-Đurić,

Milanović,

Dimitrijević-Petrović

et al. 2024

Agronomy

View full text Add to dashboard Cite

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

show abstract

Neural Network Model for Greenhouse Microclimate Predictions

Cited by 24 publications

References 26 publications

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

Prediction of Greenhouse Indoor Air Temperature Using Artificial Intelligence (AI) Combined with Sensitivity Analysis

Prediction of Greenhouse Microclimatic Parameters Using Building Transient Simulation and Artificial Neural Networks

Contact Info

Product

Resources

About