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The identification of plant species is fundamental for effective study and management of biodiversity. For automated plant species classification, a combination of leaf features like shapes, texture and color are commonly used. However, in herbariums, the samples collected for each species are often limited and during preservation step some of the feature details disappear making automated classification a challenging task. In this study, we aimed at applying machine learning techniques in automating herbarium species identification from leaf traits extracted from images of the families Annonaceae, Euphorbiaceae and Dipterocarpaceae. Furthermore, we investigated the application of Synthetic Minority Over-sampling Technique (SMOTE) in improving classifier performance on the imbalance datasets. Three machine learning techniques namely Linear Discriminant Analysis (LDA), Random Forest (RF) and …