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A novel hyperspectral waveband selection algorithm (HWSA) is proposed and applied to detect the injury severity of rice plants caused by brown planthoppers. Rice plants that were controlled or injured by brown planthoppers were sampled by the hyperspectral system. After preprocessing, the instability index (ISI) was calculated in order to measure the sensitivity of wavelengths to spectral variability, and the tradeoff index (TI) was set to remove insensitive wavelengths. The optimal wavelengths were then selected and used as inputs of the least squares support vector machine (LS-SVM) model, and the percentage of injured pixels was calculated. Different combinations of optimal wavelengths were obtained to satisfy different accuracy requirements. The wavelengths of 543.11, 568.33, and 602.35 nm were the most optimal combination, resulting in classification accuracy of 90%. The combination of 16 wavelengths, in which the wavelengths of 543.11, 568.33, and 602.35 nm were included, led to ideal classification accuracy of 98% with a suitable number of wavebands.