Neural Network Model for Greenhouse Microclimate Predictions
نویسندگان
چکیده
Food production and energy consumption are two important factors when assessing greenhouse systems. The first must respond, both quantitatively qualitatively, to the needs of population, whereas latter be kept as low possible. As a result, properly control these essential aspects, appropriate environment should maintained using computational decision support system (DSS), which will especially adaptable changes in characteristics external environment. A multilayer perceptron neural network (MLP-NN) was designed model internal temperature relative humidity an agricultural greenhouse. specific NN uses Levenberg–Marquardt backpropagation training algorithm; input variables humidity, wind speed, solar irradiance, well up three timesteps before modeled timestep. maximum errors 0.877 K 2.838%, respectively, coefficients determination 0.999 for parameters. with error predictions enable DSS provide commands actuators maintain conditions at desired levels cultivation minimum possible consumption.
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ژورنال
عنوان ژورنال: Agriculture
سال: 2022
ISSN: ['2077-0472']
DOI: https://doi.org/10.3390/agriculture12060780