On-site ion monitoring system for precision hydroponic nutrient management

Cho, Woo-Jae; Kim, Hak-Jin; Park, Soo Hyun; Kim, Dong-Wook; Ahn, Tae In; Son, Jung Eek

doi:10.1016/j.compag.2018.01.019

Cited by 65 publications

(46 citation statements)

References 20 publications

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“…[5][6] which have low absorption rates or are supplied too much. Many researchers recently have suggested to measure individual ion with ion-selective sensors and to provide insufficient ions only [1,[7][8][9]. However, their control methods are slow and cannot avoid Na+ accumulation problem caused by Fe-EDTA supply.…”

Section: Introductionmentioning

confidence: 99%

ODE Network Model for Nonlinear and Complex Agricultural Nutrient Solution System

Ban¹,

Lee²,

Ryu³

2019

2019 International Conference on Information and Communication Technology Convergence (ICTC)

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In closed hydroponic systems, periodic readjustment of nutrient solution is necessary to continuously provide stable environment to plant roots because the interaction between plant and nutrient solution changes the rate of ions in it. The traditional method is to repeat supplying small amount of premade concentrated nutrient solution, measuring total electric conductivity and pH of the tank only. As it cannot control the collapse of ion rates, recent researches try to measure the concentration of individual components to provide insufficient ions only. However, those approaches use titrationlike heuristic approaches, which repeat adding small amount of components and measuring ion density a lot of times for a single control input. Both traditional and recent methods are not only time-consuming, but also cannot predict chemical reactions related with control inputs because the nutrient solution is a nonlinear complex system, including many precipitation reactions and complicated interactions. We present a continuous network model of the nutrient solution system, whose reactions are described as differential equations. The model predicts molar concentration of each chemical components and total dissolved solids with low error. This model also can calculate the amount of chemical compounds needed to produce a desired nutrient solution, by reverse calculation from dissolved ion concentrations.

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Section: Introductionmentioning

confidence: 99%

ODE Network Model for Nonlinear and Complex Agricultural Nutrient Solution System

Ban¹,

Lee²,

Ryu³

2019

2019 International Conference on Information and Communication Technology Convergence (ICTC)

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“…Readjustment method We applied simple readjustment method in order to application of the trained model on readjustment as simple as possible because real-time and on-site readjustment is one of the biggest issue in the agricultural industry [1].…”

Section: Cmentioning

confidence: 99%

“…As the behavior of voltage along ion concentration from equation (1) and the interference effect driven from equation (2) are both logarithm, we expected that the error correction coefficient μ to show logarithmic distribution. However, quadratic regression fit much better than log regression.…”

Section: = μ( ) × (5)mentioning

confidence: 99%

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Machine Learning Approach to Remove Ion Interference Effect in Agricultural Nutrient Solutions

Ban¹,

Ryu²,

Lee³

2019

2019 International Conference on Information and Communication Technology Convergence (ICTC)

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High concentration agricultural facilities such as vertical farms or plant factories consider hydroponic techniques as optimal solutions. Although closed-system dramatically reduces water consumption and pollution issues, it has ion-ratio related problem. As the root absorbs individual ions with different rate, ion rate in a nutrient solution should be adjusted periodically. But traditional method only considers pH and electrical conductivity to adjust the nutrient solution, leading to ion imbalance and accumulation of excessive salts. To avoid those problems, some researchers have proposed ion-balancing methods which measure and control each ion concentration. However, those approaches do not overcome the innate limitations of ISEs, especially ion interference effect. An anion sensor is affected by other anions, and the error grows larger in higher concentration solution. A machine learning approach to modify ISE data distorted by ion interference effect is proposed in this paper. As measurement of TDS value is relatively robust than any other signals, we applied TDS as key parameter to build a readjustment function to remove the artifact. Once a readjustment model is established, application on ISE data can be done in real time. Readjusted data with proposed model showed about 91.6 ~ 98.3% accuracies. This method will enable the fields to apply recent methods in feasible status.

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“…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%

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

Kim

Park

et al. 2019

Sensors

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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.

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On-site ion monitoring system for precision hydroponic nutrient management

Cited by 65 publications

References 20 publications

ODE Network Model for Nonlinear and Complex Agricultural Nutrient Solution System

ODE Network Model for Nonlinear and Complex Agricultural Nutrient Solution System

Machine Learning Approach to Remove Ion Interference Effect in Agricultural Nutrient Solutions

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

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