Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

TL;DR

This paper models temperature variation in mushroom growing halls using neural networks, demonstrating that radial basis function networks outperform multilayer perceptrons in accuracy and error metrics.

Contribution

It introduces a neural network-based approach to predict temperature variations in mushroom cultivation environments, comparing different network architectures.

Findings

Radial basis function network achieved a correlation coefficient of 0.966.

RBF network had the lowest RMSE of 0.787.

RBF network was identified as the best predictor for temperature control.

Abstract

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system.