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Research on artificial neural networks to accurately predict element concentrations in nutrient solutions

文献类型： 外文期刊

作者： Zhai, Jiawei 1 ; Dong, Hongtu 1 ; Liu, Tianyang 1 ; Jin, Xiaotong 1 ; Luo, Bin 1 ; Li, Aixue 1 ; Wang, Cheng 1 ; Wang, Xiaodong 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

2.Fujian Agr & Forestry Univ, Coll Mech & Elect Engn, Fuzhou 350100, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Agr Sensors, Beijing 100097, Peoples R China

4.11 Shuguang Huayuan Zhong Lu, Beijing, Peoples R China

关键词： nutrient solution measurement; multi-ion sensing; back propagation (BP); radial basis function (RBF); particle swarm optimization (PSO); genetic algorithm (GA); gravitational search algorithm (GSA)

期刊名称：MEASUREMENT SCIENCE AND TECHNOLOGY （ 影响因子：2.4； 五年影响因子：2.3 ）

ISSN： 0957-0233

年卷期： 2023 年 34 卷 11 期

页码：

收录情况： SCI

摘要： Calcium, potassium, nitrogen, magnesium, and phosphorus, the main elements of the nutrient solution, are absorbed by plants and play an important role in plants. By measuring Ca2+, K+, Mg2+, NH4 (+), NO3 (-), HPO4 (2-), the artificial neural networks (ANNs) were used in this study to accurately calculate the concentrations of these elements. Firstly, the error sources of the calculating element concentration were analyzed based on the data of six-ion measurement experiments. Subsequently, various optimization algorithms were compared to optimize back propagation and radial basis function ANNs. Finally, the results of mean relative errors (MREs) and recovery values show that ANNs can effectively reduce the measurement error of ion sensors. From the perspective of recovery values, the prediction error of all elements can be controlled within 15%. From the perspective of MRE, except for magnesium and phosphorus elements, the improved model prediction errors of other elements were also less than 10%.

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