In order to use hyperspectral to quickly and effectively classify different types of rice, a hyperspectral imaging system was first used to obtain rice spectrum information in the 400~1000 nm spectral region, and the competitive adaptive reweighted sampling (CARS), Continuous projection algorithm (SPA), uninformative variable elimination (UVE), and shuffled frog leaping algorithm (SFLA) are used to extract characteristic wavelengths of rice spectrum information, and the extracted characteristic wavelengths are used Combined with the support vector machine (SVM) classification algorithm, six kinds of rice were identified. The research results show that the recognition rate of the classification model established using the characteristic wavelengths selected by SPA is 75.00%; the recognition rate of the classification model established using the characteristic wavelengths selected by UVE is 77.78%; the recognition rate of the classification model established using the characteristic wavelengths selected by SFLA is 52.78 %; the recognition rate of the classification model established using the characteristic wavelengths selected by CARS is 83.33%; the recognition rate of the classification model established using the spectral information in the full band reaches 77.78%. It shows that using CARS to select characteristic wavelengths can effectively replace full-band information for rice variety classification.

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Original research was done by Zhao Liu, Qi Haijun, Jin Xiu, Wen Shuxian, Li Shaowen

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