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Validation of the suitability of electrical impedance spectroscopy for the in-situ monitoring of nitrate in sandy soil

Title data

Ma, Xiaohu ; Bifano, Luca ; Oehme, Markus ; Fischerauer, Gerhard:
Validation of the suitability of electrical impedance spectroscopy for the in-situ monitoring of nitrate in sandy soil.
In: Reindl, Leonhard ; Wöllenstein, Jürgen , Informationstechnische Gesellschaft im VDE (VDE ITG); VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (VDE GMA); AMA Verband für Sensorik und Messtechnik e. V. (ed.): Sensoren und Messsysteme : Beiträge der 21. ITG/GMA-Fachtagung 10. – 11. Mai 2022 in Nürnberg. - Berlin ; Offenbach : VDI Verlag , 2022 . - pp. 352-355 . - (ITG-Fachbericht ; 303 )
ISBN 978-3-8007-5836-4

Abstract in another language

This study presents the design and development of an impedance sensor to detect the nitrate concentration in soils based on the sensitivity of the soil dielectric constant to ion conductivity and on electrical double layer effects at electrodes. The impedance of samples with nitrate-nitrogen concentrations ranging from 0 to 15 mg/L was measured at frequencies between 20 Hz and 5 kHz and noticeable conductance and susceptance effects were observed. Based on the electrical impedance spectra and the known water content, two regression models using long short-term memory (LSTM) recurrent neural networks were implemented to predict the nitrate-nitrogen concentration in artificial soil (quartz sand). A coefficient of determination of R²=0.9580 was achieved with a model including the density of the soil as a feature, and R²=0.9295 was achieved with a model that did not include the soil density as a feature. This shows that the nitrate-nitrogen concentration could be determined almost independently of soil density. Hence, the sensing system has the potential to be used in real time and in situ to monitor nitrate concentration of soils.

Further data

Item Type: Article in a book
Refereed: No
Keywords: Nitrate concentration; electrical impedance spectroscopy; artificial neural network; ANN; LSTM
Institutions of the University: Faculties > Faculty of Engineering Science
Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology
Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology > Chair Measurement and Control Technology - Univ.-Prof. Dr.-Ing. Gerhard Fischerauer
Faculties
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 13 May 2022 05:58
Last Modified: 13 May 2022 08:37
URI: https://eref.uni-bayreuth.de/id/eprint/69571