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.

叶是最常用于物种鉴定的器官之一。传统的鉴定过程涉及植物学家对单个干燥或新鲜叶片的特征进行手动分析。计算机视觉技术的最新进展已帮助基于叶的数字图像使植物家族/物种识别过程自动化。但是，大多数现有研究都集中在使用新鲜和完整叶子的数据集上。由于标本中存在受损的叶片，因此以数字化标本标本形式保存的植物的大量数据尚未有效地用于自动识别。在这项研究中，深度学习技术已被提出作为重建受损植物标本室叶片的工具，以通过增加叶片单个样本的数量来最大化数字化标本对自动植物识别任务的实用性。已经比较了两个不同卷积神经网络（CNN）家族的重建结果，以获取来自十种不同植物家族的数据，这些植物分别是Anacardiaceae，Annonaceae，Dipterocarpaceae，Ebenaceae，Euphorbiaceae，Malvaceae，Phyllanthaceae，Polygalaceae，Rubiaceae和Sapotaceae。与使用原始数据（即具有受损叶子的标本的图像）相比，使用重建的叶子图像可以将自动识别任务的性能提高20％以上。