A smart diagnostic tool based on deep kernel learning for on-site determination of phosphate, calcium, and magnesium concentration in a hydroponic system
| Autor: | Trinh Dinh Dinh, Abdul Mateen Khattak, Vu Ngoc Tuan, Wanlin Gao, Iftikhar Ahmed Saeed, Minjuan Wang, Anh Tuan Le, Wenxin Zhang |
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| Rok vydání: | 2021 |
| Předmět: |
Fertigation
Magnesium General Chemical Engineering 010401 analytical chemistry chemistry.chemical_element 02 engineering and technology General Chemistry Calcium 021001 nanoscience & nanotechnology Phosphate 01 natural sciences 0104 chemical sciences chemistry.chemical_compound chemistry Ionic strength Kernel (statistics) Standard addition Environmental science 0210 nano-technology Biological system Magnesium ion |
| Zdroj: | RSC Advances. 11:11177-11191 |
| ISSN: | 2046-2069 |
| DOI: | 10.1039/d1ra00140j |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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