Abstract:
To optimize photosynthesis and crop growth in greenhouses, it is essential to predict the
concentration and consumption of carbon dioxide. In this study, we aimed to anticipate the
CO2 concentration in two distinct greenhouses: one equipped with a cooling system and the
other without. Over a month, we meticulously measured temperature, relative humidity, and
CO2 concentration in these greenhouses at the Experimental Research Unit for Renewable
Energies in Ghardaïa, Algeria. To achieve this, we used an Arduino board coupled with
various sensors. The collected data were then used to train an artificial neural network,
employing the Long Short-Term Memory (LSTM) algorithm for prediction. The analysis of
the obtained results demonstrates the model’s reliability, with R2
and MSE parameter values
ranging between 0.95% and.1%. Special attention will be given to the potential use of these
models for improving agricultural production, economic evaluation, and environmental
impact.
