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Soil pH and Humidity Classification Based on GRU-RNN Via UWB Radar Echoes

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Communications, Signal Processing, and Systems (CSPS 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 571))

Abstract

This paper proposed a new method to classify soil with different pH values and humidity based on GRU-RNN via ultra-wideband (UWB) radar echoes. Five categories of UWB soil echoes with different soil parameter, the pH values and water contents, are collected and investigated by GRU-RNN. And the simulation experiment results indicate that compared with LSTM-RNN, GRU-RNN has a better classification performance and has a shorter execution time. This can be an evidence that GRU-RNN method is more suitable for the study of other soil parameters.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61671138, 61731006), and was partly supported by the 111 Project No. B17008.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave., Chengdu, 611731, China

    Chenghao Yang, Tiantian Wang & Jing Liang

Authors
  1. Chenghao Yang
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  2. Tiantian Wang
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  3. Jing Liang
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Corresponding author

Correspondence to Chenghao Yang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  3. School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China

    Zhenyu Na

  5. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China

    Min Jia

  6. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Yang, C., Wang, T., Liang, J. (2020). Soil pH and Humidity Classification Based on GRU-RNN Via UWB Radar Echoes. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_257

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  • DOI: https://doi.org/10.1007/978-981-13-9409-6_257

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9408-9

  • Online ISBN: 978-981-13-9409-6

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