Validation testing of an ion-specific sensing and control system for precision hydroponic macronutrient management

Choi

et al. 2019

Sensors

Self Cite

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 R2 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 R2 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 R2 = 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.

Section: Introductionmentioning

confidence: 99%

Fusion of Spectroscopy and Cobalt Electrochemistry Data for Estimating Phosphate Concentration in Hydroponic Solution

Choi

et al. 2019

Sensors

Self Cite

“…Finally, the sensor array was composed of three ISEs for NO 3 , three ISEs for K, two ISEs for Ca, one reference electrode, and one conductivity probe. It has been reported that the ISEs prepared in the study are applicable for hydroponic solutions [10,17,18,23,26,27]. The performance characteristics of the ISEs reported in the previous studies are summarized in Table 2.…”

Section: Preparation Of the Sensor Arraymentioning

confidence: 97%

“…In closed hydroponics, the management of the reused solutions is mostly conducted by the conductivity and pH probes. However, the probes can only provide a total ion activity and pH, so the imbalance of nutrient ratios may occur in reused nutrient solutions due to the lack of information about the individual ion concentrations [8][9][10][11][12]. This makes the crop quality and productivity decrease.…”

Section: Introductionmentioning

confidence: 99%

“…One such method is a two-point normalization (TPN) method in conjunction with the use of the Nernst equation that consists of a sensitivity adjustment followed by an offset adjustment applied to all of the signal data measured with the ISEs [18,23,26]. In previous studies, the TPN method was employed and shown to be effective in compensating for the signal drifts of a sensor array consisting of NO 3 , K, and Ca ISEs which were used for measuring hydroponic solutions [10,17,18,23,26,27]. However, the TPN method is relatively weak for the interference because the standard curve for the TPN is constructed based on the simplified Nernst equation.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Signal-Processing Method Based on Neural Network for Prediction of NO3, K, Ca, and Mg Ions in Hydroponic Solutions Using an Array of Ion-Selective Electrodes

Cho

Park

et al. 2019

Sensors

Self Cite

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 (NO 3 , 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 NO 3 , 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.

“…In addition, there is still a lack of an effective phosphate ion-selective electrode that allows to determine phosphate conveniently and precisely. Therefore, most approaches, created a reaction between cobalt electrode surface and phosphate to quantitatively detect phosphate ions from the treated soil or aqueous samples, for which a sufficient selective sensitivity could be confirmed [6]- [8]. Nevertheless, the cobalt electrode has not yet been used in practical applications because of some weaknesses such as the instability of the electrode signal band.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Standard Addition Cobalt Electrochemistry Data Fusion for Determining Phosphate Concentration in Hydroponic Solution

et al. 2020

Phosphate is one of the major elements affecting agricultural production. The accurate determination of phosphate concentration essential for plant growth, especially in a hydroponics system, allows regulating the balanced and suitable range set of nutrients to plants efficiently. This study proposed a data fusion model based on 70 samples for calibration and 30 samples for predicting concentrations of phosphate in an eggplant nutrient solution. Three multivariate analysis methods i.e. partial least squares model (PLS), Gaussian process regression (GPR), and artificial neural network (ANN) were studied and compared for their performance efficiencies. The results showed that combining the multivariate standard addition method (MSAM) in acquiring data from cobalt electrodes and ANN data fusion model came up with satisfactory outcomes. Both the method provided good performance with R 2 values of 0.98 and 0.96, and the root mean square error (RMSE) of 50 and 66 mg. L −1 respectively in calibration and evaluation tests. These values were much higher than those of conventional processing techniques. Moreover, the normal direct calibration method in acquisition signal from cobalt electrodes was also applied, which provided R 2 values of 0.7 to 0.8. These high values are sufficient for development to measure phosphate concentration in hydroponic solutions. INDEX TERMS Phosphate sensing, multi-sensor data fusion, multivariate standard addition method (MSAM), partial least squares model (PLS), Gaussian process regression (GPR), neural network-ANN.