Enhancing predictive accuracy of inside temperature and humidity in an agricultural greenhouse using data-driven modeling with artificial neural networks

This work presents an original and innovative approach by combining greenhouse cooling with artificial intelligence models to ensure food security.The study is divided into two parts: Experimental and theoretical.In the first part, a cooling system was implemented in a tunnel-type agricultural greenhouse and compared to a control system.The cooling system consists of multiple fans powered by two solar panels.Data was collected using an acquisition system (Arduino) over approximately one month.This data, along with external data, was utilized to predict the internal temperature of the greenhouse using backpropagation neural network models.The results obtained demonstrate the reliability of the model based on all tests, as evidenced by the coefficient of determination (R 2 ) and the mean square error (MSE) for the prediction.