Abstract
In closed hydroponics, fast and continuous measurement of individual nutrient concentrations is necessary to improve water- and nutrient-use efficiencies and crop production. Ion-selective electrodes (ISEs) could be one of the most attractive tools for hydroponic applications. However, signal drifts over time and interferences from other ions present in hydroponic solutions make it difficult to use the ISEs in hydroponic solutions. In this study, hybrid signal processing combining a two-point normalization (TPN) method for the effective compensation of the drifts and a back propagation artificial neural network (ANN) algorithm for the interpretation of the interferences was developed. In addition, the ANN-based approach for the prediction of Mg concentration which had no feasible ISE was conducted by interpreting the signals from a sensor array consisting of electrical conductivity (EC) and ion-selective electrodes (NO3, K, and Ca). From the application test using 8 samples from real greenhouses, the hybrid method based on a combination of the TPN and ANN methods showed relatively low root mean square errors of 47.2, 13.2, and 18.9 mg∙L−1 with coefficients of variation (CVs) below 10% for NO3, K, and Ca, respectively, compared to those obtained by separate use of the two methods. Furthermore, the Mg prediction results with a root mean square error (RMSE) of 14.6 mg∙L−1 over the range of 10–60 mg∙L−1 showed potential as an approximate diagnostic tool to measure Mg in hydroponic solutions. These results demonstrate that the hybrid method can improve the accuracy and feasibility of ISEs in hydroponic applications.
Highlights
Hydroponics is a cultivation method that grows plants using nutrient solutions composed of water and nutrient salts without soil
Based on the complementary properties of the two-point normalization (TPN) and Artificial Neural Network (ANN) methods, in this study, we proposed a hybrid signal processing approach to effectively compensate for the signal drifts and interferences from other ions, thereby improving the accuracy of Ion-selective electrodes (ISEs) in hydroponic applications
The hybrid method improved the accuracy and the precision of the prediction of the ion concentrations with the lowest root mean square error (RMSE) of 47.2, 13.2, and 18.9 mg·L−1 and CVs below 10% for NO3, K, and Ca, respectively
Summary
Hydroponics is a cultivation method that grows plants using nutrient solutions composed of water and nutrient salts without soil. Hydroponics has been widely and rapidly utilized in agricultural industries because it is the most intensive and effective production method that can be designed to support year-round production with high yields and good quality [1,2]. Hydroponics is usually classified into open and closed types. In closed hydroponics, which collects drainage solutions and reuses these by replenishing water and nutrients, the use and discharge of water and nutrients are less than for open hydroponics [5,6,7]. Current practices for closed hydroponics still have several limitations, as described below
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