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Automated Plant Species Identification Using Leaf Shape-Based Classification Techniques: A Case Study on Iranian Maples

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Abstract

Foliar characteristics, especially the overall leaf shape, are useful features for the taxonomic identification of plants. Computer-aided plant species identification systems make it possible to investigate a large number of leaves in a short period of time. In this study, a fully automatic system was developed to accurately classify eight species of maples (Acer L.) in Iran using the leaf shape characteristics of harvested leaves. Maples show a broad range of leaf morphology, and the provided dataset is a diverse collection of simple leaves which can be a good representative of woody plant leaves with any overall shape and margin pattern. The applied method consisted of preprocessing, feature extraction and classification steps. A new method for petiole removal and a new feature as teeth indicator are presented in preprocessing and feature extraction parts, respectively. Using support vector machine (SVM) classifier, experimental results showed a high accuracy of 98.60% ± 1.20 for the identification of maple species. Focusing on extracting robust features, the proposed method offers considerable potential to speed up expert identification of maples with high accuracy whose output can be applied to computational botany, conservation planning and natural resource management.

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Acknowledgements

We are thankful to curators of the HKS and TUH herbaria for making herbarium facilities available for our study. We would like to thank the members of the Plant Biosystematics laboratory at University of Tehran for their assistance in the field and collecting plant materials.

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Authors and Affiliations

  1. Center of Excellence in Phylogeny of Living Organisms and Department of Plant Science, School of Biology, College of Science, University of Tehran, Tehran, Iran

    Mojgansadat Mohtashamian

  2. Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

    Mojgansadat Mohtashamian & Ali Masoudi-Nejad

  3. Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

    Kaveh Kavousi

  4. Machine Learning Laboratory, Computer Engineering Department, Sharif University of Technology, Tehran, Iran

    Mahmood Karimian

  5. Department of Geography, Geomatics and Environment, University of Guelph, Guelph, ON, Canada

    Faisal Moola

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  1. Mojgansadat Mohtashamian
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  2. Mahmood Karimian
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  5. Ali Masoudi-Nejad
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Correspondence to Mojgansadat Mohtashamian.

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Mohtashamian, M., Karimian, M., Moola, F. et al. Automated Plant Species Identification Using Leaf Shape-Based Classification Techniques: A Case Study on Iranian Maples. Iran J Sci Technol Trans Electr Eng 45, 1051–1061 (2021). https://doi.org/10.1007/s40998-020-00398-2

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  • DOI: https://doi.org/10.1007/s40998-020-00398-2

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