Abstract: Fresh grapes come in a variety of different shapes and colors. To address the problem of reduced accuracy of picking point positioning when the grape picking robot picks different varieties of fresh grapes, a multi-variety fresh grape picking method based on deep-learning is proposed. In this paper, firstly, a PSPNet（MobileNetv2） semantic segmentation model is used to segment the grape image, a region of interest is set above the grapes, the stalk edge information is extracted within the region of interest using adaptive thresholding of the stalk direction Canny edge detection, then the straight line segment on the stalk edge is detected using the Hough transform and a straight line is fitted. Finally the intersection of the fitted straight line with the horizontal symmetry axis of the region of interest is taken as the picking point. The picking point location experiment was carried out on 360 grape images of four varieties of Crescent, Sun Rose, Red Raisin and Black Goldfinger under three lighting conditions as sunny day with light, sunny day with light backlight and sunny day with shade. The results showed that the picking point positioning accuracy was 91.94%, the positioning time was 187.47 ms and the picking success rate was 85.5% in the simulated experiments. This study achieves rapid picking point positioning for multiple varieties of fresh grapes and provides technical support for picking point positioning in grape picking robots.