Abstract: Abstract: Two key challenges can be the accurate separation of the overlapping fruits and the positioning of the plant stem during the automatic picking of honey pomelo. However, the existing approaches to separate the overlapping apples and citrus are not suitable for the overlapping honey pomelo, particularly no positioning function of the stem so far. In this study, new image recognition was proposed to combine with the progressive center and stem positioning in natural scene images, in order to improve the recognition rate of honey pomelo. Firstly, the principal component analysis (PCA) was used to determine the principal components of the color pixel values in the target area and the distribution intervals of each component. The PCA was also utilized to reduce the data dimensionality. As such, the rotation matrix was obtained to convert the image from the RGB to the principal component space. The distribution intervals of the color principal components were used to evaluate and filter the pixels of the honey pomelo. A binarization was then performed to obtain a binary image. Secondly, the edge information of the color image after filtering the background was selected to preliminarily divide the binary image. If a pixel was an edge point on the color image, the corresponding point on the binary image was set for the background pixel. Before separating, a white area was determined to contain the multiple honey pomelos. In addition, the separation operation was performed on the area, only when there were multiple honey pomelos. Thirdly, a progressive center positioning was adopted to locate the center of each honey pomelo in the overlapping honey pomelo area. An operation was also conducted from the top, bottom, left, and right directions to obtain the center of each honey pomelo. Finally, the separation point of the overlapping area was determined to realize the recognition, where the edge points of the white area were traversed along the edge, in order to calculate the distance between each edge point and the center points of two adjacent honey pomelos. Since the stem of the honey pomelo was located near the top extension line of the long axis passing through the center point in the longitudinal section, the central point was used to locate the stem area with a smaller degree of obscuration and normal suspension. A total of 50 images in natural scenes were selected to verify the model. The test results showed that the average recognition rate of the new recognition was 94.02% in the natural scene. Furthermore, the stem areas were accurately located for the honey pomelos, whose stems were not blocked or closer to the camera. Consequently, the new recognition can be widely expected to transfer to the embedded development system and an automatic picking platform with the laboratory honey pomelo model for picking experiments. This finding can also provide a strong reference to accurately recognize the overlapping fruits for the picking robot of honey pomelo.