Next Article in Journal
Spirulina platensis Biofertilization for Enhancing Growth, Photosynthetic Capacity and Yield of Lupinus luteus
Previous Article in Journal
Vascular Bundle Characteristics of Different Rice Variety Treated with Nitrogen Fertilizers and Its Relation to Stem Assimilates Allocation and Grain Yield
Article

Neural Network Model for Greenhouse Microclimate Predictions

1
Department of Agriculture, University of Patras, 26504 Patras, Greece
2
Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece
3
Laboratory of Atmospheric Physics, Department of Physics, University of Patras, 6500 Patras, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Muhammad Sultan
Agriculture 2022, 12(6), 780; https://doi.org/10.3390/agriculture12060780
Received: 10 May 2022 / Revised: 25 May 2022 / Accepted: 26 May 2022 / Published: 28 May 2022
Food production and energy consumption are two important factors when assessing greenhouse systems. The first must respond, both quantitatively and qualitatively, to the needs of the population, whereas the latter must be kept as low as possible. As a result, to properly control these two essential aspects, the appropriate greenhouse environment should be maintained using a computational decision support system (DSS), which will be especially adaptable to changes in the characteristics of the external environment. A multilayer perceptron neural network (MLP-NN) was designed to model the internal temperature and relative humidity of an agricultural greenhouse. The specific NN uses Levenberg–Marquardt backpropagation as a training algorithm; the input variables are the external temperature and relative humidity, wind speed, and solar irradiance, as well as the internal temperature and relative humidity, up to three timesteps before the modeled timestep. The maximum errors of the modeled temperature and relative humidity are 0.877 K and 2.838%, respectively, whereas the coefficients of determination are 0.999 for both parameters. A model with a low maximum error in predictions will enable a DSS to provide the appropriate commands to the greenhouse actuators to maintain the internal conditions at the desired levels for cultivation with the minimum possible energy consumption. View Full-Text
Keywords: greenhouse; neural networks model; multilayer perceptron; decision support system; Levenberg–Marquardt; temperature modeling; relative humidity modeling greenhouse; neural networks model; multilayer perceptron; decision support system; Levenberg–Marquardt; temperature modeling; relative humidity modeling
Show Figures

Figure 1

MDPI and ACS Style

Petrakis, T.; Kavga, A.; Thomopoulos, V.; Argiriou, A.A. Neural Network Model for Greenhouse Microclimate Predictions. Agriculture 2022, 12, 780. https://doi.org/10.3390/agriculture12060780

AMA Style

Petrakis T, Kavga A, Thomopoulos V, Argiriou AA. Neural Network Model for Greenhouse Microclimate Predictions. Agriculture. 2022; 12(6):780. https://doi.org/10.3390/agriculture12060780

Chicago/Turabian Style

Petrakis, Theodoros, Angeliki Kavga, Vasileios Thomopoulos, and Athanassios A. Argiriou. 2022. "Neural Network Model for Greenhouse Microclimate Predictions" Agriculture 12, no. 6: 780. https://doi.org/10.3390/agriculture12060780

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop