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Research on optimal predicting model for the grading detection of rice blast

  • Ya-hui Luo
  • Ping JiangEmail author
  • Kai Xie
  • Fu-jie Wang
Regular Paper
  • 9 Downloads

Abstract

Rice blast is a worldwide disease of rice that is an important reason for the reduction of rice yield. In this paper, “Lingliangyou 268” was selected as the research object. The spectral data were measured by a Landmark Spectrum instrument. The spectral characteristics of the original spectrum, derivative spectrum and logarithmic spectrum of different grades of rice blast were studied. A new method for rice blast grading based on sensitive bands was proposed. Then, the method of system clustering method, BP neural network and probabilistic neural network were used to establish the rice blast classification prediction model, respectively. Comparing the three models, the classification effect based on probabilistic neural network is the best. In the training samples, the logarithmic spectral classification accuracy is 97.8%. In the test samples, the logarithmic spectral classification accuracy is 75.5%.

Keywords

Rice blast High spectral Grading-detection 

Notes

Acknowledgements

This paper is supported by the Foundation Item: Technology plan of Hunan Province (2016NK2117).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Copyright information

© The Optical Society of Japan 2019

Authors and Affiliations

  1. 1.College of engineeringHunan Agricultural UniversityChangshaChina
  2. 2.Southern Regional Collaborative Innovation Center for Grain and Oil Crops in ChinaChangshaChina

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