Abstract
This paper aims at researching a rapid, lossless, accurate and high-robustness classification model with the combination of hyperspectral image technology and stoichiometry to sole some problems in seed purity detection. The hyperspectral data is subject to the wave-band selection with the combination of stoichiometry, the mathematical model set up by using hyperspectral image is upgraded, and the seed purity is also tested. The experiment result indicates that: as the characteristic space of the original training set sample is enlarged by adding the new samples of 11.0%-12.8% of the predicted sample set in real time, the prediction accuracy of the upgraded model on the seed purity can be improved. Therefore, the online model upgrading strategy based on ISVDD can be used to improve the stability and generalization ability of the model well, and the requirements on instantaneity and accuracy of model upgrading in the actual production can be met.
