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Prediction of NO_3, K, Ca, and Mg Ions in Hydroponic Solutions using Neural Network Model with an Array of Ion-Selective Electrodes

文献类型： 会议论文

第一作者： Woo-Jae Cho

作者： Woo-Jae Cho 1 ; Hak-Jin Kim 1 ; Dae Hyun Jung 1 ;

作者机构： 1.Dept of Biosystems and Biomaterials Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea

关键词： closed hydroponics;hydroponic solution;ion-selective electrode;neural network;nonlinearregression.

会议名称： American Society of Agricultural and Biological Engineers Annual International Meeting

主办单位：

页码： 2919-2926

摘要： The measurement of individual nutrient concentrations is crucial in closed hydroponics because the correction of each deficient nutrient can allow both improved efficiency offertilizer use and increased time of use of the nutrient solution. In this study, back propagation artificial neural network (ANN) algorithm combined with a two-point normalization method was employed to predict NO_3, K, Ca, and Mg ions. Data from a sensor array of electrical conductivity (EC), and ion selective electrodes (NO_3,K, and Ca) was used as inputs of ANN model. For the training and validation of the model, a set of samples with a background referring the Hoagland's solution were prepared by a fractional factorial design with three levels of concentrations for four ions. To compensate the drifts during the measurements, a two-point normalization method was applied prior to each sample measurement by an automated test stand. The prediction model for Mg ions showed a low coefficient of determination (R~2=0.37) from thetraining. However, the ANN models for NO 3, K, and Ca ions showed a high modelling capacity with high coefficients of determination (R~2>0.9). In application of the models to hydroponic samples from lettuce and paprika growing beds, mean absolute relative errors of the ANN models were 3.1,16.7, and 4.1% for NO_3, K, and Ca ions, respectively.

分类号： S-532

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