Virtual Ion Selective Electrode for Online Measurement of Nutrient Solution Components

Chen, Feng; Wei, Dali; Tang, Yongning

doi:10.1109/jsen.2010.2060479

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…NO3-N, K, Ca 이온센서는 최근에 상당히 안정적으로 사용할 수 있다고 보고되고 있으므로 (Kim et al, 2006;Feng et al, 2011;Morard, 1996) 이 이온의 제어는 센서를 이용한다는 전제하에 측정이 불가능한 P, Mg 이온의 조절을 위하여 센서를 이 용할 수 있는 것과의 이온흡수 상관관계를 분석하였다. 1, 2차 시험 공통으로 PO4는 N와의 상관계수가 가장 높게 나타났고 Mg 는 Ca와 가장 높은 상관관계가 있는 것으로 나타났다( Table 2).…”

Section: 결과 및 고찰unclassified

“…개별이온 센서에 관한 연구는 ISE(ion selective Electrodes), ISFET(ion-selective field-effect transistors), Optoedes(광학센서를 이용하여 흡광 도에 따라 이온농도 측정), VISE(virtual ion selective electrode), 전자혀(Potentiometric Electronic Tongue) 등 다양한 시도가 이루어 지고 있지만 (Gieling et al, 2005;Kim et al, 2006;Gutierrez et al, 2008;Feng et al, 2011) 센서의 정확성과 재현성이 낮고 보정 주 기가 짧아 실용적인 적용은 어려운 것이 현실이다 (Morard, 1997;Gieling et al, 2005). 그럼에도 이에 대한 연구는 꾸준히 이루 어지고 있어 NO3-N, K, Ca 이온센서는 최근에 상당히 안정적으로 사용할 수 있는 것으로 보고되고 있다 (Morard, 1996;Kim et al, 2006;Feng et al, 2011). 그러나 SO4와 PO4는 이온선택성 센서로 측정할 수 없다고 하였고, Mg는 안정된 결과치를 얻을 수 없었다고 하였다 (Kim et al, 2006)…”

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Control of Mg and P Ion Concentration as a Precondition to Use N, K and Ca Ion Sensors in Closed Hydroponics

Choi,

Yeo,

Rhee

et al. 2016

HST

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Recycling nutrient solutions in closed hydroponic production systems is usually accompanied by an imbalance of nutrient solutions when concentration is controlled according to electrical conductivity (EC) levels. This study investigated whether it was possible to automatically control the concentrations of five essential elements nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg) using only N, K and Ca ion sensors. N, P, K, Ca, and Mg uptake was measured in the nutrient solution, and relationships between absorbed ions were analyzed through twice-repeated experiments in lettuce. Results confirmed that the pattern of PO4 ion uptake was similar that of N, and the pattern of Mg ion uptake was similar that of Ca. PO4 ion uptake was most highly correlated with N, and Mg was most highly correlated with Ca. Regression coefficients of N and PO4 were significantly different at 1.04 and 0.55, respectively, but were similar between Ca and Mg at 0.35 and 0.40, respectively. Additional experiments were conducted to measure nutrient uptake in pak choi and rose plants, both to confirm the results from the first experiment in lettuce, and to assess possible application to other crops. Coefficients of determination both for N and PO4, and Ca and Mg were considerably high (R 2 = 0.86) in cultured pak choi, and similar results were observed in cultured rose (R 2 = 0.87 and 0.73, respectively). Regression coefficients for cultured pak choi were 0.56 and 0.24, respectively, and for rose were 0.51 and 0.16, respectively. Although the results obtained for N and PO4 were not consistent between the lettuce experiments, N and PO4 have similar regression coefficients for all crops. No common coefficient was found between Ca and Mg.

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Section: 결과 및 고찰unclassified

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Control of Mg and P Ion Concentration as a Precondition to Use N, K and Ca Ion Sensors in Closed Hydroponics

Choi,

Yeo,

Rhee

et al. 2016

HST

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“…The neural network was used to eliminate interference caused by changing temperature, especially, for the enhancement of sensors with ion-selective electrodes [23], interference of other ions with the primary ion [24], [25], fluctuating ionic strength [26], [27], and drifting potential [17], [28], [29] etc. Besides, the data fusion models have made a novel breakthrough in processing sensor signals to improve sensors' characteristics [30], fusing big data sensing [31], developing virtual sensors for non-availability of commercial electrodes ion types [32], [33]. Gutierrez et al [34] proposed advantageous multiple ISEs and ANN model to monitor nutrients in hydroponic solutions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Various approaches have been proposed to resolve the technical challenges of applying ISEs in closed hydroponic systems, such as applying particular sampling [ 12 , 13 ], calibrating techniques [ 14 , 15 ], and compensating interferences supported by machine learning techniques (for example, artificial neural network (ANN) and deep neural network (DNN)) [ 16 , 17 , 18 , 19 , 20 ]. Apart from that, preprocessing techniques as such principle component analysis (PCA) [ 21 ], independent component analysis (ICA) [ 22 , 23 , 24 ], partial least squares (PLS) model [ 25 , 26 ], and the kernel model as Gaussian Process (GP) model, support vector machine model (SVM) [ 27 ], etc. have also been used.…”

Section: Introductionmentioning

confidence: 99%

“…However, none of technical strategies has radically resolved the drawbacks of ISEs. Other investigations focused on predicting the ions that are not available through an ion-selective electrode (as such

, and Mg) were examined by fusing the datasets acquired from an array of available ISEs [ 20 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

Tuan

Khattak

Zhu

et al. 2020

Sensors

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Ion-selective electrodes (ISEs) have recently become the most attractive tools for the development of efficient hydroponic systems. Nevertheless, some inherent shortcomings such as signal drifts, secondary ion interferences, and effected high ionic strength make them difficult to apply in a hydroponic system. To minimize these deficiencies, we combined the multivariate standard addition (MSAM) sampling technique with the deep kernel learning (DKL) model for a six ISEs array to increase the prediction accuracy and precision of eight ions, including NO3−, NH4+, K+, Ca2+, Na+, Cl−, H2PO4−, and Mg2+. The enhanced data feature based on feature enrichment (FE) of the MSAM technique provided more useful information to DKL for improving the prediction reliability of the available ISE ions and enhanced the detection of unavailable ISE ions (phosphate and magnesium). The results showed that the combined MSAM–feature enrichment (FE)–DKL sensing structure for validating ten real hydroponic samples achieved low root mean square errors (RMSE) of 63.8, 8.3, 29.2, 18.5, 11.8, and 8.8 mg·L−1 with below 8% coefficients of variation (CVs) for predicting nitrate, ammonium, potassium, calcium, sodium, and chloride, respectively. Moreover, the prediction of phosphate and magnesium in the ranges of 5–275 mg·L−1 and 10–80 mg·L−1 had RMSEs of 29.6 and 8.7 mg·L−1 respectively. The results prove that the proposed approach can be applied successfully to improve the accuracy and feasibility of ISEs in a closed hydroponic system.

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Virtual Ion Selective Electrode for Online Measurement of Nutrient Solution Components

Cited by 15 publications

References 21 publications

Control of Mg and P Ion Concentration as a Precondition to Use N, K and Ca Ion Sensors in Closed Hydroponics

Control of Mg and P Ion Concentration as a Precondition to Use N, K and Ca Ion Sensors in Closed Hydroponics

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

Combination of Multivariate Standard Addition Technique and Deep Kernel Learning Model for Determining Multi-Ion in Hydroponic Nutrient Solution

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