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Article

Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation

1
Digital Science, Faculty of Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei
2
Department of Environmental Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei
*
Author to whom correspondence should be addressed.
Academic Editor: Anastasios Doulamis
Sensors 2021, 21(13), 4549; https://doi.org/10.3390/s21134549
Received: 20 May 2021 / Revised: 21 June 2021 / Accepted: 23 June 2021 / Published: 2 July 2021
(This article belongs to the Section Intelligent Sensors)
With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of plant leaves data, herbarium datasets have an inherent challenge to deal with the sheets containing other non-plant objects such as color charts, barcodes, and labels. Even for the plant part itself, a combination of different overlapping, damaged, and intact individual leaves exist together with other plant organs such as stems and fruits, which increases the complexity of leaf trait extraction and analysis. Focusing on segmentation and trait extraction on individual intact herbarium leaves, this study proposes a pipeline consisting of deep learning semantic segmentation model (DeepLabv3+), connected component analysis, and a single-leaf classifier trained on binary images to automate the extraction of an intact individual leaf with phenotypic traits. The proposed method achieved a higher F1-score for both the in-house dataset (96%) and on a publicly available herbarium dataset (93%) compared to object detection-based approaches including Faster R-CNN and YOLOv5. Furthermore, using the proposed approach, the phenotypic measurements extracted from the segmented individual leaves were closer to the ground truth measurements, which suggests the importance of the segmentation process in handling background noise. Compared to the object detection-based approaches, the proposed method showed a promising direction toward an autonomous tool for the extraction of individual leaves together with their trait data directly from herbarium specimen images. View Full-Text
Keywords: semantic segmentation; object detection; leaf extraction; connected component analysis; plant species identification; herbarium leaf dataset; phenotypic features; leaf measurements; deep learning semantic segmentation; object detection; leaf extraction; connected component analysis; plant species identification; herbarium leaf dataset; phenotypic features; leaf measurements; deep learning
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MDPI and ACS Style

Hussein, B.R.; Malik, O.A.; Ong, W.-H.; Slik, J.W.F. Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation. Sensors 2021, 21, 4549. https://doi.org/10.3390/s21134549

AMA Style

Hussein BR, Malik OA, Ong W-H, Slik JWF. Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation. Sensors. 2021; 21(13):4549. https://doi.org/10.3390/s21134549

Chicago/Turabian Style

Hussein, Burhan R., Owais A. Malik, Wee-Hong Ong, and Johan W.F. Slik 2021. "Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation" Sensors 21, no. 13: 4549. https://doi.org/10.3390/s21134549

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