Plant species identification based on leaf venation features using SVM - Agus Ambarwari; Qadhli Jafar Adrian; Yeni Herdiyeni; Irman Hermadi (Universitas Ahmad Dahlan, 2020)
The purpose of this study is to identify plant species using leaf venation features. Leaf venation features were obtained through the extraction of leaf venation features. The leaf image segmentation was performed to obtain the binary image of the leaf venation which is then determined the branching point and ending point. From these points, the extraction of leaf venation feature was performed by calculating the value of straightness, a different angle, length ratio, scale projection, skeleton length, number of segments, total skeleton length, number of branching points and number of ending points. So that from the extraction of leaf venation features 19 features were obtained. Identification of plant species was carried out using Support Vector Machine (SVM) with RBF kernel. The learning model was built using 75% of the training data. The testing results using 25% of the data on the training model, obtained an accuracy of 82.67%, with an average of precision of 84% and recall of 83%.