Advances in nutrient management modelling and nutrient concentration prediction for soilless culture systems

Abstract

In closed-loop soilless culture systems (SCS), ion concentration and ionic balance are important factors to be considered for stable management of nutrient solutions. For maintaining appropriate ion concentration and ion balance, various techniques of nutrient analysis and prediction are required. Through nutrient management modelling, nutrient variations in the closed-loop soilless culture systems using nutrient replenishment methods can be better understood and predicted. Deep learning algorithms could be a methodology to predict ion concentrations using environments and growth data. A trained deep learning model has been found to accurately estimate ion concentration and balance in closed-loop SCS. Applications of theoretical modelling and artificial intelligence can thus be useful for the nutrient management of closed-loop SCS in greenhouses and vertical farms.