Electrical Conductivity in Deep–trough Hydroponics

A model is presented that predicts pH and electrical conductivity (EC) changes in the root zone of lettuce (Lactuca sativa cv. Vivaldi) grown in a deep–trough hydroponic system. A feedforward neural network is the basis of that modeling. The neural network model has nine inputs (pH, EC, nutrient solution temperature, air temperature, relative humidity, light intensity, plant age, amount of added acid, and amount of added base) and two outputs (pH and EC at the next time step). The most suitable and accurate combination of network architecture and training method was one hidden layer with nine hidden nodes, trained with the quasi–Newton backpropagation algorithm. The model proved capable of predicting pH at the next 20–minute time step within 0.01 pH units and EC within 5 Scm–1. Simpler prediction methods, such as linear extrapolation and the “lazy man” prediction (in which “prediction” is the value of the previous time step), gave comparable accuracy much of the time. However, they performed poorly in situations where the control actions of the system had been activated and produced relatively rapid changes in the predicted parameters. In those cases, the neural network model did not encounter any difficulties predicting the rapid changes. Thus, the developed model successfully identified dynamic processes in the root zone of the hydroponic system and accurately predicted one–step–ahead values of pH and EC.