Leaf vein segmentation is an important step in leaf pattern analysis, which is of great significance for soybean variety identification and phenotype research. On account of the complicated vein structure of soybean leaves and the low contrast of the leaf area where the veins are located, it is generally impossible to achieve ideal leaf vein segmentation results only using gray information. This study presents a soybean vein segmentation method combining the multi-scale gray unconstrained hit-or-miss transform (UHMT) algorithm and the processing method based on the hue data of HSI color space. In this method, the gray information in RGB color space and the hue data in HSI color space are used to segment the global leaf veins and local primary and secondary veins from soybean leaf images, respectively. The former uses iterative threshold segmentation to extract the leaf area and eliminates interference factors such as the outer contour and the petiole through expansion and corrosion to obtain the leaf area image. Then, the multi-scale gray UHMT algorithm is employed to obtain the global leaf vein image. Considering the poor performance of primary and secondary vein segmentation, we use hue data to enlarge the discrepancies in gray values between veins pixels and other pixels to realize the segmentation of local primary and secondary veins. The obtained global and local vein images are fused into the final soybean leaf vein image. Moreover, this study utilizes soybean leaf images in the soybean leaf image database, SoyCultivar, to verify the effectiveness of the algorithm. The results indicate that this algorithm is better than existing leaf vein segmentation methods as it can not only extract soybean leaf veins completely but also well eliminate the background, leaf contours, petioles, and other irrelevant components.