A method for predicting rice quality based on hyperspectral analysis of rice seed
Author:
Affiliation:

1.National Key Laboratory of Corp Genetic Improvement, Huazhong Agricultural University,Wuhan 430070, China;2.College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China

Clc Number:

TP391.41;TS210.7

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    The content of amylose, crude protein and water is an important index to measure the quality of rice grain. The transmittance and reflectance spectral data of rice grains from 100 rice core germplasm resources were collected using a near-infrared hyperspectral camera, and the spectral parameters were extracted to study the method for the non-destructive testing of quality traits in rice seed. Index of rice quality components was measured using a near infrared grain analyzer after the rice kernel was shelled. A model for predicting index of rice quality was established using the spectral parameters of rice grains as independent variables and index of rice quality as dependent variables. The results showed that the modeling effect of transmission spectrum was better than that of reflection spectrum when a single spectral model was used. Combined with characteristic spectral sets of transmission and reflection, the R2 of the model for predicting crude protein, amylose and water was increased from 0.74 to 0.91, from 0.40 to 0.69, and from 0.53 to 0.68, respectively. It is indicated that the modeling effect can be improved by using both spectrum of transmission and reflection, and index of rice quality can be predicted nondestructively by using the spectral parameters of rice grains.

    Reference
    Related
    Cited by
Get Citation

赵爽,宋京燕,陈国兴,宋鹏,冯慧,杨万能. A method for predicting rice quality based on hyperspectral analysis of rice seed[J]. Jorunal of Huazhong Agricultural University,2023,42(3):211-219.

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:October 06,2022
  • Revised:
  • Adopted:
  • Online: June 20,2023
  • Published: