Automated Classification of Tropical Plant Species Data Based on Machine Learning Techniques and Leaf Trait Measurements
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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 Support Vector Machine (SVM) were applied with/without SMOTE. For Annonaceae species, the best accuracy was 56% by LDA after applying SMOTE. For Euphorbiaceae, the best accuracy was 79% by SVM without SMOTE. For inter-species classification between Annonaceae and Euphorbiaceae, the best accuracy of 63% was achieved by LDA without SMOTE. An accuracy of 85% was achieved by LDA for Dipterocarpaceae species while 91% accuracy was obtained by both RF and SVM for inter-family classification between the two balanced datasets of Annonaceae and Euphorbiaceae. The results of this study show the feasibility of using extracted traits for building accurate species identification models for Family Dipterocarpaceae and Euphorbiaceae, however, the features used did not yield good results for Annonaceae family. Furthermore, there was no significant improvement when SMOTE technique was applied.
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