The surface crack identification and detection of a corn kernel are studied based on digital image processing. Cold Cathode Fluorescent Lamps(CCFL) are chosen to construct the image capturing illumination environment

and a set of image acquisition system of the corn kernel is established. Then

a surface crack detection method is developed based on morphology of the corn kernel. A binary image including the cracks

boundary and noises is picked up with horizontal and vertical Sobel operators. Then

a majority of non-crack information is eliminated by image subtraction after finding the tip of the corn kernel using its morphology. Finally

the cracks are extracted and the absolute and relative lengths of cracks are calculated according to the crack length and position. A detecting experiment is carried out by 50 kernels with cracks and 50 kernels without cracks selected from NongDa-4967 and NongDa-3138 (two novel varieties of corn seed developed by China Agricultural University) respectively. The results indicate that the detecting accuracy are 94% and 90%. It can meet the requirement of the accuracy of surface crack detection of corn kernel.