Automatic Identification of First-Order Veins and Corolla Contours in Three-Dimensional Floral Images

Defining and quantifying corolla traits are essential for studying corolla shape variation. Three-dimensional (3D) images of corollas contain comprehensive information regarding corolla structures and are optimal for studying corolla shapes. Conventionally, corolla traits are identified and quantified manually from 3D images. Manual identification is time consuming and labor intensive. In this study, approaches are proposed to automatically identify first-order veins and corolla contours in 3D corolla images. The first-order veins of the corollas were identified using Hessian of Gaussian and Dijkstra’s algorithm. The contours of the corollas were identified using vector harmony and node distance thresholding. A total of 130 3D images of 28 species in the subtribe Ligeriinae were collected and used to test the proposed approaches. The successful detection rate reached 86.54%. Two derived traits, contour–vein ratio and corolla angle, were defined and quantified using the first-order veins and corolla contour results to investigate the relationship between corolla shapes and pollination types of the subtribe Ligeriinae. Analyses revealed that the mean corolla contour, mean absolute corolla angle, and mean contour–vein ratio of the ornithophilic species were significantly smaller compared with the other species. The mean corolla contour, mean corolla angle, and mean contour–vein ratio of the melittophilic species were significantly larger compared with those of the ornithophilic species. The proposed method was also applied to certain Gesneriaceae species in the subtribes Gloxiniinae, Streptocarpinae, and Didymocarpinae. The results revealed that the method could be applied to most fresh sympetalous flowers for identifying first-order veins and corolla contours.