Jung, Dae-Hyun Kim, Hak-Jin Kim, Hyoung Seok Choi, Jaeyoung Kim, Jeong Do Park, Soo Hyun 2024-01-19T20:00:49Z 2024-01-19T20:00:49Z 2021-09-02 2019-06-01 1424-8220 https://pubs.kist.re.kr/handle/201004/119896 Phosphate is a key element affecting plant growth. Therefore, the accurate determination of phosphate concentration in hydroponic nutrient solutions is essential for providing a balanced set of nutrients to plants within a suitable range. This study aimed to develop a data fusion approach for determining phosphate concentrations in a paprika nutrient solution. As a conventional multivariate analysis approach using spectral data, partial least squares regression (PLSR) and principal components regression (PCR) models were developed using 56 samples for calibration and 24 samples for evaluation. The R-2 values of estimation models using PCR and PLSR ranged from 0.44 to 0.64. Furthermore, an estimation model using raw electromotive force (EMF) data from cobalt electrodes gave R-2 values of 0.58-0.71. To improve the model performance, a data fusion method was developed to estimate phosphate concentration using near infrared (NIR) spectral and cobalt electrochemical data. Raw EMF data from cobalt electrodes and principle component values from the spectral data were combined. Results of calibration and evaluation tests using an artificial neural network estimation model showed that R-2 = 0.90 and 0.89 and root mean square error (RMSE) = 96.70 and 119.50 mg/L, respectively. These values are sufficiently high for application to measuring phosphate concentration in hydroponic solutions. English MDPI PRINCIPAL COMPONENTS REGRESSION SENSOR DATA FUSION NUTRIENT SOLUTION SOIL PROPERTIES SYSTEM WATER PERFORMANCE PHOSPHORUS MANAGEMENT NITROGEN Fusion of Spectroscopy and Cobalt Electrochemistry Data for Estimating Phosphate Concentration in Hydroponic Solution Article 10.3390/s19112596 1 SENSORS, v.19, no.11 SENSORS 19 11 scie scopus 000472133300174 2-s2.0-85067791559 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article PRINCIPAL COMPONENTS REGRESSION SENSOR DATA FUSION NUTRIENT SOLUTION SOIL PROPERTIES SYSTEM WATER PERFORMANCE PHOSPHORUS MANAGEMENT NITROGEN phosphate sensing Multi-sensor data fusion hybrid sensor system Feed-forward back-propagation ANN