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Reconstruction of damaged herbarium leaves using deep learning techniques for improving classification accuracy
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Abstract:
Leaf is one of the most commonly used organs for species identification. The traditional identification process
involves a manual analysis of individual dried or fresh leaf’s features by the botanists. Recent advancements in
computer vision techniques have assisted in automating the plants families/species identification process based
on the digital images of leaves. However, most of the existing studies have focused on using datasets for fresh and
intact leaves. A huge amount of data for preserved plants in the form of digitized herbaria specimens have not
been effectively utilized for the task of automated identification because of the presence of damaged leaves in
specimens. In this study, deep learning techniques have been proposed as a tool for reconstructing the damaged
herbarium leaves in order to maximize the usefulness of the digitized specimens for automated plant identification
task by increasing the number of individual samples of leaves. The reconstruction results of two different
families of convolution neural networks (CNNs) have been compared for data from ten different plant families
namely Anacardiaceae, Annonaceae, Dipterocarpaceae, Ebenaceae, Euphorbiaceae, Malvaceae, Phyllanthaceae,
Polygalaceae, Rubiaceae and Sapotaceae. The performance of automated identification task was improved by
more than 20% using the reconstructed leaves images as compared to using the original data (i.e. images of
specimens with damaged leaves). This work evidently suggests that deep learning techniques can be utilized for
reconstruction of damaged leaves even on a challenging herbarium leaves dataset.
Description:
Date:
2021
Authors:
Burhan Rashid Hussein
Owais Ahmed Malik
Ong, Wee-Hong
Slik, Johan Willem Frederik
Publisher:
Elsevier B.V.
