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

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: https://doi.org/10.3390/s23042172

Official URL: Volltext

Project information

Project financing: VolkswagenStiftung

Abstract in another language

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
Refereed: Yes
Keywords: Nitrate concentration; electrical impedance spectroscopy; EIS; recurrent artificial neural network; RNN; long short-term memory; LSTM; Random Forest
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
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 08 Mar 2023 07:25
Last Modified: 05 Oct 2023 07:59
URI: https://eref.uni-bayreuth.de/id/eprint/73668