In order to solve the problems that noise intensity of each band for plant hyperspectral image is different and noise exists in both spatial and spectral domains, a sparse representation denoising method is proposed based on the grouped three-dimensional (3D) discrete cosine transform (DCT) dictionary. Firstly, the spectral characteristics of the plants are analyzed and the bands are grouped according to the spectral correlation. Secondly, local mean standard deviation of eliminating edges is used to estimate the noise standard deviation of hyperspectral images, which provides the reference threshold for denoising algorithm. Finally, a sparse representation denoising method based on 3D DCT dictionary is constructed for denoising plant hyperspectral images. Experimental results show that, comparing with the original data and the denoising method of two-dimensional (2D) DCT dictionary, the average signal-to-noise ratios of the noise evaluation by the proposed method are improved by 18.2 dB and 9.2 dB in the spectral domain. Therefore, the proposed method can denoise not only in the spatial domain but also in the spectral domain.