Quantity Detection of Kernels in an Ear Corn Based on Machine Vision

Abstract

A high efficient method is provided to count the number of kernels in an ear corn. PC camera captured a sequence of images around an ear corn using a simple device. The Otsu's algorithm was applied for background segmentation. An object processing area was obtained after contour extraction. The x-direction cumulative histogram was used to extract every row of the ear corn. The y-direction cumulative histogram was used to detect the number of corn kernels in a row. The number of rows was got by matching the edge of the current ear row with the first one. The time of detecting kernels for an ear corn was below 2 minutes and the average accuracy was about 95% in experiment. It shows that this method can detect the quantity of kernels directly in an ear corn quickly and effectively using a low-cost device.