It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

中文翻译：

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利用人工神经网络预测铝箔加热隧道内的空气温度。

正确预测温室的小气候对于控制和作物管理非常重要。精确预测温室温度一直是研究的重点，因为内部温度是影响作物生长的最重要因素之一。人工神经网络（ANN）是进行预测的强大工具。我们研究的目的是精心设计一个模型，该模型可以使用ANN预测铝箔加热隧道内的温度变化。在位于克拉科夫农业大学附近的加热箔隧道中进行了实验研究。获得的结果已作为人工神经网络的数据。进行的研究证实了人工神经网络作为进行内部温度预测的工具的有用性。在所有经过测试的网络中，最好的是三层Perceptron型网络，在隐藏层有10个神经元。该网络有40个输入和一个输出（预测的内部温度）。当网络输入以前的历史内部温度时，将使用外部温度，太阳辐射强度，风速和预报时间。对于测试数据集，这些人工神经网络具有最低的均方根误差（RMSE）值（RMSE值= 3.7°C）。