Identification of tip cap and germ surface of corn kernel using computer vision

Two problems were researched, the identification of tip cap and germ surface of corn kernel, which are important to inspect its quality by computer vision. By analyzing morphological features of tip cap and intensity feature of germ surface, two algorithms were established. The algorithms provide overall successful identification of 92.50% for tip cap and 89.58% for germ surface feature among four corn varieties of xhg, xn12, wn14 and wc. This constitutes a great step towards computing feature parameters of corn kernel.