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The measurement of individual nutrient concentrations is crucial in closed hydroponics because the correction of each deficient nutrient can allow both improved efficiency of fertilizer use and increased time of use of the nutrient solution. In this study, back propagation artificial neural network (ANN) algorithm combined with a two-point normalization method was employed to predict NO3, K, Ca, and Mg ions. Data from a sensor array of electrical conductivity (EC), and ion selective electrodes (NO3, K, and Ca) was used as inputs of ANN model. For the training and validation of the model, a set of samples with a background referring the Hoagland‘s solution were prepared by a fractional factorial design with three levels of concentrations for four ions. To compensate the drifts during the measurements, a two-point normalization method was applied prior to each sample measurement by an automated test stand. The prediction model for Mg ions showed a low coefficient of determination (R2=0.37) from the training. However, the ANN models for NO3, K, and Ca ions showed a high modelling capacity with high coefficients of determination (R2>0.9). In application of the models to hydroponic samples from lettuce and paprika growing beds, mean absolute relative errors of the ANN models were 3.1, 16.7, and 4.1% for NO3, K, and Ca ions, respectively.

A recirculating hydroponic system based on EC-control is not efficient in managing the composition of individual ions in the solution, thereby resulting in a high increase in total concentration of the nutrients and an imbalance of the nutrient salts. This study reports on the development and evaluation of a computer-controlled system with an array of ion-selective electrodes (ISEs) and four diaphragm pumps for direct measurement and individual control of macronutrients in recirculating hydroponic solutions. The ability of three PVC membrane-based ISEs for simultaneous determination of NO 3 -, K + , and Ca 2+ ions in the closed soilless system was evaluated. A computer program was developed with NI LabVIEW, which could calculate the volumes of individual nutrient stock solutions to be supplied based on real-time measurements of NO 3 -K-Ca ions, and vary the injection times of the metering pumps working at constant injection rates. A performance test of the developed sensing and control system was conducted by varying the concentrations of macronutrients in original lettuce solution contained in a hydroponic tank. The results showed that the NO 3 –K-Ca ISEs exhibited satisfactory performance in measuring nutrient concentrations in hydroponic solution and the target concentrations were prepared as expected when using the automated nutrient management system, showing linear relationships with high determination coefficient of >0.9 between the ISE- dosing methods and standard analyzers.