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

Kim, Hak-Jin; Choi, Jaeyoung; Kim, Jeong Do; Park, Soo Hyun

doi:10.3390/s19112596

Cited by 16 publications

(24 citation statements)

References 52 publications

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“…The first hidden layer used 45 nodes and the hyperbolic tangent (Tanh) activation, while the second hidden layer used 30 nodes and the rectified linear unit (ReLU) activation function ( Table 2). In the previous study [20], the results were verified through various combinations of active functions in the hidden nodes and parameter values, but no significant correlation was found. For training, the Levenberg-Marquardt algorithm was used, a gradient descent method for avoiding local minima and overfitting [20] of the 73,440 samples used for model development; training used 70%, validation 15%, and testing 15%.…”

Section: Neural Network Based Temperature Prediction Modelmentioning

confidence: 84%

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Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

Park

Kim

et al. 2020

Sensors

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Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R2 of 0.94). In addition, the method using an algorithm based on an OFNN was proposed for optimizing considered six window-opening behavior. Three case studies confirmed the optimization performance of OFNN in the nonlinear model and verified the performance through simulations. Finally, a control system based on this logic was used in a field experiment for six days by comparing two greenhouses driven by conventional control logic and the developed control logic; a comparison of the results showed RMSEs of 3.01 °C and 2.45 °C, respectively. It confirmed the improved control performance in comparison to a conventional ventilation control system.

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Section: Neural Network Based Temperature Prediction Modelmentioning

confidence: 84%

“…Models based on neural networks are suitable for both linear and nonlinear modeling and have been applied to greenhouse environment modeling and control logic [18][19][20]. Many studies have reported reliable results in environmental prediction modeling using artificial neural networks [18,[21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

Park

Kim

et al. 2020

Sensors

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“…Recently, fusion technique has been applied to detect phosphate in hydroponic solutions. For example, Jung et al [35] used two types of sensors, i.e. NIR spectroscopy and cobalt electrode, to develop fusion for detecting PO 4 ion concentration in a paprika nutrient solution.…”

Section: Introductionmentioning

confidence: 99%

“…Though many approaches based on unique or associative fusion techniques have been used, the cobalt electrode has not been deployed yet in practical applications. This is because of some weaknesses such as it needs frequently abrading and electrode surface treating constantly to ensure induce oxidation reaction, which affects changes in the electrode signal band [35], [36]. Moreover, MSAM is considered to be the most suitable method for compensating the shortcomings of ISEs sensing systems.…”

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

Cited by 16 publications

References 52 publications

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control

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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