Short Communication: Geometric morphometric analysis of leaf venation in four Shorea species for identification using Digital Image Processing

Abstract

Abstract. Ariawan I, Herdiyeni Y, Siregar IZ. 2020. Short Communication: Geometric morphometric analysis of leaves venation in Shorea spp. for identification using Digital Image Processing. Biodiversitas 21: 3303-3309. Shorea is one of the genera of the Dipterocarpaceae family which consists of more than 190 species. Massive exploitation of forests has threatened the sustainability of Shorea in nature. A total of 156 species has been listed on the IUCN (International Union for Conservation of Nature) red list. From the 156 species, 59.6% are in the critically endangered category, so urgent conservation is needed. However, during collection of Shorea at the seedling phase for conservation  purposes, it is often difficult to distinguish among them that can cause errors in their collection process. To avoid these errors, identification needs to be done, usually based on plant  leaf and flower  morphology. Leaves are easier because they have the main features that distinguish each plant species, one of which is the venation structure. Geometric morphometric techniques are a modern approach recognized as useful for the identification of species in many plants. Geometric morphometrics analyzes the position of the venation point using coordinate geometry values. This research was aimed to extract venation features of Shorea leaves using a geometric morphometric approach. The extraction process result in some features, such as straightness, different angle, length ratio, scale projection, and secondary nerves. On extracted features, an analysis was then performed to find out the best features in classifying species of Shorea spp. The results of this study indicated that the geometric morphometric approach could extract the value of the features of straightness, different angle, length ratio, scale projection, and secondary nerves. The secondary nerve feature is the best feature because it can distinguish between fourcommonly planted species of Shorea spp. (S. acuminata, S. leprosula, S. ovalis, and S. selanica). By using the support vector machine classification technique to identify species of Shorea spp., the classification results obtained an average accuracy of 84.46%.