Virtual Ion Selective Electrode for Online Measurement of Nutrient Solution Components

The measurement of multiple components in nutrient solution is prerequisite for optimal control of nutrient solution. The current measurement methods of nutrient solution estimate the concentrations of components in nutrient solution based on pH and electronic conductivity (EC) values, which lead to large errors. In this paper, a virtual ion selective electrode (VISE) approach is proposed to online measure the hard-to-measure components in nutrient solution with highly improved performance (e.g., accuracy and speed). In order to effectively model VISE, the correlation among nutrient solution components has to be analyzed, which is significantly challenging due to its uncertainty and complexity. In this study, the variation regularities of the nutrient solution components are experimentally investigated. The correlation among the nutrient solution components is found according to the intrinsic analysis of vegetable growth. In our approach, least squares support vector machine (LS-SVM) is adopted to fuse the sensor data to achieve fast computing and global optimum. In addition, to improve the estimation accuracy and reduce the computational complexity of LS-SVM, a formula is introduced based on the characteristics of ion selective electrode (ISE) to represent the regularization parameter, which is critical in determining the tradeoff between the model complexity and fitting errors. The experimental results show that the proposed VISE model is effective and offers a beneficial reference for multiple component measurement.