A method of information fusion for identification of rice seed varieties based on hyperspectral imaging technology

Accurate, rapid, and nondestructive identification of rice seed varieties has great significance for agriculture and food security, a method based on information fusion and artificial fish swarm algorithm (AFSA) combined with the hyperspectral imaging (HSI) of five kinds of rice seeds was proposed in this work. First, the spectral and image data were obtained from HSI, and the spectral data were preprocessed by detrending. Then, bootstrapping soft shrinkage (BOSS), variable iterative space shrinkage approach, successive projections algorithm, and principal component analysis were adopted to select feature variables from the spectral and image data. Next, the support vector machine (SVM) model was constructed based on the spectral and image feature variables. In order to further improve the classification accuracy of single feature model, the model based on fused feature was developed and it was finally optimized by AFSA. The research showed that the feature variables (114 spectral variables and 11 image variables) selected by BOSS were representative, and the model accuracy based on BOSS spectral and image features reached 91.48% and 70%, respectively. The performance of the SVM model based on fused feature was improved significantly, and the model accuracy reached 97.22%. After AFSA optimization, the model accuracy finally reached 99.44%. The above result confirmed that using AFSA to optimize the model based on fused feature could be a promising method to identify rice seed varieties. Practical application Rapid and accurate identification of rice seed varieties contributes to establishment of online rice seed identification system. HSI combined with information fusion and AFSA can overcome the disadvantage of low accuracy of traditional nondestructive testing methods. The method proposed in this article can be recommended to be widely popularized in farms, grain markets, and market regulators.