Effective detection of peel defects on fruit was always the most important in automatic non-destructive detection of fruit. And, accurate segmentation of peel defects was a premise to effectively grade the fruit based on size of defect. However, since fruit surface usually has a larger curvature change, the non-uniform reflection from fruit surface is probably caused in terms of the same incident light source, and the accurate segmentation of peel defects will be affected. ‘Pinggu’ peaches were applied as the research object, and a watershed segmentation method combining morphological gradient reconstruction with internal and external markers was proposed to segment the defects on fruit peel. First, R channel image was extracted and background was removed by mask template obtained from NIR image. Then, gradient image was obtained by morphological gradient operation, and gradient reconstruction was performed by using the gradient image to remove some small noises on the fruit surface. Next, internal and external marker operations were used to obtain the marker image. Finally, defects on peel were segmented by using the standard watershed algorithm. For the investigated 525 sample images including seven peel types, a 96.8% successful recognition rate was achieved. The experimental results showed that a watershed segmentation algorithm combining morphological gradient reconstruction with marker extraction could be effectively used to segment the peel defects on peach and the performance of algorithm was not affected by non-uniform illumination on peach surface. However, defect segmentation rate needed to be further improved.