Advertisement

Soil pH Classification Based on LSTM via UWB Radar Echoes

  • Tiantian WangEmail author
  • Fangqi Zhu
  • Jing Liang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

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.

Keywords

Soil pH UWB radar echoes LSTM 

Notes

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.

References

  1. 1.
    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.CrossRefGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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.Google Scholar
  4. 4.
    Liang J, Liu X, Liao K. Soil moisture retrieval using UWB echoes via fuzzy logic and machine learning. IEEE Internet Things J. 2017Google Scholar
  5. 5.
    Dewberry B. Monostatic radar module reconfiguration and evaluation tool (mrm-ret) pulson \(\textregistered \). Time Domain Corp; 2012Google Scholar
  6. 6.
    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.CrossRefGoogle Scholar
  7. 7.
    Tury W, Horton R. Soil physics. Hoboken: Wiley & Sons Inc; 2004.Google Scholar
  8. 8.
    Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT press; 2015.zbMATHGoogle Scholar
  9. 9.
    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; 2017Google Scholar
  10. 10.
    Understanding LSTM Networks. http://colah.github.io
  11. 11.
    Schmidhuber J. Deep learning in neural networks: an overview. Neural Networks. 2015;61:85–117.CrossRefGoogle Scholar
  12. 12.
    Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks. 2005;18(5–6):602–10.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.University of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations