Abstract
This paper proposed a new method to classify soil pH based on long short-term memory (LSTM) via ultra-wideband (UWB) radar echoes. The main contribution of this paper is to provide a solution by incorporating the LSTM into the field experiment related to UWB based on soil pH echoes. Five types of UWB soil echoes with different pH values are collected and investigated using LSTM approach. Finally, the analysis of results shows that LSTM method presents a good classification performance with a short execution time and the data features do not need to be extracted manually. The high accuracy rate also shows that LSTM method is beneficial to the study of other soil parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lambot S, Slob EC, van den Bosch I, Stockbroeckx B, Vanclooster M. Modeling of ground-penetrating radar for accurate characterization of subsurface electric properties. IEEE Trans Geosci Remote Sens. 2004;42(11):2555–68.
Lambot S, Slob EC, van den Bosch I, Stockbroeckx B, Scheers B, Vanclooster M. Estimating soil electric properties from monostatic ground-penetrating radar signal inversion in the frequency domain. Water Resour Res. 2004;40(4)
Liu M, Zhu F, Liang J. Channel modeling based on ultra-wide bandwidth (UWB) radar in soil environment with different pH values. In: 2014 Sixth international conference on wireless communications and signal processing (WCSP); 2014. IEEE, p. 1–6.
Liang J, Liu X, Liao K. Soil moisture retrieval using UWB echoes via fuzzy logic and machine learning. IEEE Internet Things J. 2017
Dewberry B. Monostatic radar module reconfiguration and evaluation tool (mrm-ret) pulson \(\textregistered \). Time Domain Corp; 2012
Liang J, Zhu F. Soil moisture retrieval from UWB sensor data by leveraging fuzzy logic. Accepted for publication on IEEE Access, https://doi.org/10.1109/ACCESS.2018.2840159.
Tury W, Horton R. Soil physics. Hoboken: Wiley & Sons Inc; 2004.
Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT press; 2015.
Velickovic P, Karazija L, Lane ND, Bhattacharya S, Liberis E, Lio P, Vegreville M. Cross-modal recurrent models for weight objective prediction from multimodal time-series data. ArXiv e-prints; 2017
Understanding LSTM Networks. http://colah.github.io
Schmidhuber J. Deep learning in neural networks: an overview. Neural Networks. 2015;61:85–117.
Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks. 2005;18(5–6):602–10.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (61671138, 61731006), and was partly supported by the 111 Project No. B17008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, T., Zhu, F., Liang, J. (2020). Soil pH Classification Based on LSTM via UWB Radar Echoes. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_92
Download citation
DOI: https://doi.org/10.1007/978-981-13-6504-1_92
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6503-4
Online ISBN: 978-981-13-6504-1
eBook Packages: EngineeringEngineering (R0)