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Estimation Model Based on Spectral-Reflectance Data

  • Tao ChiEmail author
  • Guangpu Cao
  • Bingchun Li
  • Zi Kerr Abdurahman
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)

Abstract

A new estimation model based on BPNN is proposed in the thesis to recognize the characteristics of the spectral-reflectance data rapidly and estimate soil salt content accurately. Take the case of USGS Digital Spectral Library to estimate salt content of different soils, BP neural network model has been optimized, improved and realized in MATLAB. In this experiment, the prediction accuracy of multiple BPNN model reaches 83%, which is superior to traditional BP neural network model and multi-factor orthogonal regression analysis, indicating that the multiple BPNN model is suitable for the complex regression analysis of nonlinear spectral data and the high-precision estimation model of soil salt content. This method could also be used in embedded system, and design real-time testing equipment of soil salt content.

Keywords

Big spectral data Digital spectral library BP neural network Soil salt content estimation 

Notes

Acknowledgements

This work is partly supported by the national natural science fund (61561027) and the shanghai natural science fund (16ZR1415100). Thanks to Key Laboratory of Fisheries Information Ministry of Agriculture.

References

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tao Chi
    • 1
    • 2
    Email author
  • Guangpu Cao
    • 1
  • Bingchun Li
    • 3
  • Zi Kerr Abdurahman
    • 3
  1. 1.College of Information TechnologyShanghai Ocean UniversityShanghaiChina
  2. 2.Key Laboratory of Fisheries InformationMinistry of AgricultureShanghaiChina
  3. 3.School of Computer Science and TechnologyKashgar UniversityXinjiangChina

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