Abstract: Abstract: Fruit identification and location are the primary tasks and difficulties for fruit-harvesting robot, whose accuracy closely relates to the efficiency of robot. Many scholars, at home and abroad, have carried out a large number of researches on overlapping fruits and have achieved some initial results. However, these studies are limited to static conditions, which are not available for moving state. In recent years, some scholars have studied dynamic identification of harvesting robot and have proved that image processing time could be effectively reduced by using the correlation of a series of images, but the recognition of overlapping fruits is not included. In order to solve the problem of identification of overlapping fruits in moving state and real-time harvesting of robot, the research of fast tracking identification of overlapping fruits is conducted. Firstly, the collected image should be segmented and denoised by mophology method, then the location of the center is determined by calculating the maximum value of minimum distance between the edge of contour and points within the circle. Four scanning directions are defined in order to improve the timeliness; in direction A, the points are scanned from left to right and from top to bottom; in direction B, from right to left and from top to bottom; in direction C, from right to left and from bottom to top; in direction D, from left to right and from bottom to top. The radius is determined by calculating the distance from the center to the edge contour, but in the case of overlapping fruit, the distance from the center to the edge may be the distance to another apple, so a new method is used to avoid this situation. The distances from the center to the edge in different directions are calculated and the minimum distance is used as the final radius. After that, the template of the follow-up match is intercepted by the center and radius. The experiment proves that the algorithm can accurately find the center and radius of overlapping fruit. Then, the centers of images which are collected continuously should be determined. According to the center position of each image and the sampling time of the robot, the motion path of the robot is fitted and predicted. In this experiment, seven-order curve fits robot trajectory well. The processing scope of the next image is determined by radius and anticipated path. Finally, fast normalized cross-correlation match is used to identify overlapping fruit. Fast normalized cross-correlation method is simple and eliminating the issue of light sensitivity, so it is applied to apple images under different light intensities, besides, its matching result is also accurate when the image is slightly displaced and rotated. 268×252 pixel images are used in the experiments and the apples approximately account for 55% of the entire image; matching recognition time is 0.185 s without anticipation, and after anticipation, the time is 0.133 s, which means that the processing time of the improved algorithm is reduced by 28.1%. The comparison experiment demonstrates that the new method accelerates the speed of robot and makes it more practical.