Abstract
Calcium, phosphate, and magnesium are essential nutrients for plant growth. The in situ determination of these nutrients is an important task for monitoring them in a closed hydroponic system where the nutrient elements need to be individually quantified based on ion-selective electrode (ISE) sensing. The accuracy issue of calcium ISEs due to interference, drift, and ionic strength, and the unavailability of phosphate and magnesium ISEs makes the development of these ion detecting tools hard to set up in a hydroponic system. This study modeled and evaluated a smart tool for recognising three ions (calcium, phosphate, and magnesium) based on the automatic multivariate standard addition method (AMSAM) and deep kernel learning (DKL) model. The purpose was to improve the accuracy of calcium ISEs, determining phosphate through cobalt electrochemistry, and soft sensing of magnesium ions. The model provided better performance in on-site detecting and measuring those ions in a lettuce hydroponic system achieving root mean square errors (RMSEs) of 12.5, 12.1, and 7.5 mg L-1 with coefficients of variation (CVs) below 5.0%, 7.0%, and 10% for determining Ca2+, H2PO4 -, and Mg2+ in the range of 150-250, 100-200, and 20-70 mg L-1 respectively. Furthermore, the DKL was implemented for the first time in the third platform (LabVIEW) and deployed to determine three ions in a real on-site hydroponic system. The open architecture of the SDT allowed posting the measured results on a cloud computer. This would help growers monitor their plants' nutrients conveniently. The informative data about the three mentioned ions that have no commercial sensors so far, could be adapted to the other components to develop a fully automated fertigation system for hydroponic production.