Abstract: The identification of wheat heading at flowering stage can be used to guide the later water and fertilizer management, disease control, yield prediction and other aspects. In order to realize accurate and automatic ear counting, this paper proposes a new ear counting method based on color features. The color of wheat ear is very close to that of leaf and stem at heading flowering stage, and the common color features can not segment wheat ear effectively. In this paper, we extracted wheat ear effectively by color histogram equalization and red green normalized difference index. Aiming at the problem of wheat spike adhesion in the image, this paper uses the improved Harris corner detection algorithm to verify the wheat images taken at vertical angle and 45° angle respectively. Through the sample image counting experiment, the accuracy is 96.06% and 94.74% respectively. The results showed that there were obvious color differences in wheat ears, leaves and stems after equalization, and the color features could be used to extract the images of wheat ears at flowering stage in field environment. The detection of skeleton intersection point after wheat ear thinning can accurately count the conglutinated wheat ears with high counting accuracy, which can be used to reflect the situation of wheat heading in this period.