Research on artificial neural networks to accurately predict element concentrations in nutrient solutions
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 Ca 2+ , K + , Mg 2+ , NH 4 + , NO 3 − , HPO 4 2− , the artificial neural networks (ANNs) were used in this study to accuratel...
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| Published in | Measurement science & technology Vol. 34; no. 11; p. 115121 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
01.11.2023
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| Online Access | Get full text |
| ISSN | 0957-0233 1361-6501 1361-6501 |
| DOI | 10.1088/1361-6501/ace4e5 |
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| Abstract | 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 Ca 2+ , K + , Mg 2+ , NH 4 + , NO 3 − , HPO 4 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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| AbstractList | 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 Ca 2+ , K + , Mg 2+ , NH 4 + , NO 3 − , HPO 4 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%. |
| Author | Li, Aixue Dong, Hongtu Wang, Xiaodong Wang, Cheng Zhai, Jiawei Luo, Bin Jin, Xiaotong Liu, Tianyang |
| Author_xml | – sequence: 1 givenname: Jiawei orcidid: 0000-0001-6261-4129 surname: Zhai fullname: Zhai, Jiawei – sequence: 2 givenname: Hongtu surname: Dong fullname: Dong, Hongtu – sequence: 3 givenname: Tianyang surname: Liu fullname: Liu, Tianyang – sequence: 4 givenname: Xiaotong surname: Jin fullname: Jin, Xiaotong – sequence: 5 givenname: Bin surname: Luo fullname: Luo, Bin – sequence: 6 givenname: Aixue surname: Li fullname: Li, Aixue – sequence: 7 givenname: Cheng surname: Wang fullname: Wang, Cheng – sequence: 8 givenname: Xiaodong surname: Wang fullname: Wang, Xiaodong |
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