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Hyperspectral imaging which integrating both spectroscopic and imaging techniques with higher spatial and spectral resolution, has been developed to study the physical characteristics, chemical constituents and distributions of different quality attributes. It’s difficult to further analyze because of the adverse effects produced by the curvature of spherical objects in the process of hyperspectral images acquirement. Its suitability was illustrated in a specific case of apple fruits. This study proposes a method for correcting the light intensity of radiation nonuniform on the apple fruits. Firstly, the original hyperspectral images were corrected into the reflectance hyperspectral images based on black and white reference images, resulting in reducing the influence of illumination and the dark current of the camera. Then, the mean spectra extracted from roundness region of interest (ROI) in centre area of hyperspectral image were used to develop calibration models by using partial least squares (PLS) regression. The correlation coefficient and root mean square errors of calibration were found to be 0.9305 and 0.4331, respectively. After applying the proposed correction, the spectra of the pixels in hyperspectral image were performed to calculate the sugar content of corresponding pixels. Finally, the visualization of sugar content distribution in apple was achieved by using pseudo-color mapping. The results demonstrated that the correction method was proved to be effective for eliminating the adverse effects produced by the curvature of the fruit on the intensity of the radiation. The hyperspectral imaging has a great potential to be a nondestructive and rapid tool for the quantitative measurement of sugar content distribution for apple.