Evaluation of electrical impedance spectra by long short-term memory to estimate nitrate concentrations in soil

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007561
URN to cite this document: urn:nbn:de:bvb:703-epub-7561-2

Title data

Ma, Xiaohu ; Bifano, Luca ; Fischerauer, Gerhard:
Evaluation of electrical impedance spectra by long short-term memory to estimate nitrate concentrations in soil.
In: Sensors. Vol. 23 (2023) Issue 4 . - 2172.
ISSN 1424-8220
DOI der Verlagsversion: https://doi.org/10.3390/s23042172

	Format: PDF Name: sensors-23-02172.pdf Version: Published Version Available under License Creative Commons BY 4.0: Attribution
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Project information

Project title:	Project's official title Project's id Open Access Publizieren No information
Project financing:	VolkswagenStiftung

Abstract

Monitoring the nitrate concentration in soil is crucial to guide the use of nitrate-based fertilizers. This study presents the characteristics of an impedance sensor used to estimate the nitrate concentration in soil based on the sensitivity of the soil dielectric constant to ion conductivity and on electrical double layer effects at electrodes. The impedance of synthetic sandy soil 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. Long short-term memory (LSTM), a variant of recurrent artificial neural networks (RNN), was investigated with respect to its suitability to extract nitrate concentrations from the measured impedance spectra and additional physical properties of the soils such as mass density and water content. Both random forest and LSTM were tested as feature selection methods. Then numerous LSTMs were trained to estimate the nitrate concentrations in the soils. To increase estimation accuracy, hyperparameters were optimized with Bayesian optimization. The resulting optimal regression model showed coefficients of determination between true and predicted nitrate concentrations as high as 0.95. Thus, it could be demonstrated that the system has the potential to monitor nitrate concentrations in soils in real time and in situ

Further data

Item Type:	Article in a journal
Keywords:	Nitrate concentration; electrical impedance spectroscopy; EIS; recurrent artificial neural network; RNN; long short-term memory; LSTM; Random Forest
DDC Subjects:	600 Technology, medicine, applied sciences > 620 Engineering
Institutions of the University:	Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology > Chair Measurement and Control Technology - Univ.-Prof. Dr.-Ing. Gerhard Fischerauer Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-7561-2
Date Deposited:	18 Mar 2024 09:05
Last Modified:	18 Mar 2024 09:05
URI:	https://epub.uni-bayreuth.de/id/eprint/7561

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