Dataset Information

Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum).

ABSTRACT: Premise:Equisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools. Methods:We hypothesize that aerial stem nodes can provide enough information to distinguish among Equisetum hyemale, E. laevigatum, and E . ×ferrissii, the latter being a hybrid between the other two. An object detector was trained to find nodes on a given image and to distinguish E. hyemale nodes from those of E. laevigatum. A classifier then took statistics from the detection results and classified the given image into one of the three taxa. Both detector and classifier were trained and tested on expert manually annotated images. Results:In our exploratory test set of 30 images, our detector/classifier combination identified all 10 E. laevigatum images correctly, as well as nine out of 10 E. hyemale images, and eight out of 10 E. ×ferrissii images, for a 90% classification accuracy. Discussion:Our results support the notion that computer vision may help with the identification of herbarium specimens once enough manual annotations become available.

SUBMITTER: Pryer KM

PROVIDER: S-EPMC7328651 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Using computer vision on herbarium specimen images to discriminate among closely related horsetails (<i>Equisetum</i>).

Pryer Kathleen M KM Tomasi Carlo C Wang Xiaohan X Meineke Emily K EK Windham Michael D MD

Applications in plant sciences 20200601 6

<h4>Premise</h4><i>Equisetum</i> is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools.<h4>Methods</h4>We hypothesize that aerial stem nodes can provide enough information to distinguish among <i>Equisetum hyemale</i>, <i>E. laev ...[more]

PMID: 32626613

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Project description:BackgroundMorphological leaf traits are frequently used to quantify, understand and predict plant and vegetation functional diversity and ecology, including environmental and climate change responses. Although morphological leaf traits are easy to measure, their coverage for characterising variation within species and across temporal scales is limited. At the same time, there are about 3100 herbaria worldwide, containing approximately 390 million plant specimens dating from the 16th to 21st century, which can potentially be used to extract morphological leaf traits. Globally, plant specimens are rapidly being digitised and images are made openly available via various biodiversity data platforms, such as iDigBio and GBIF. Based on a pilot study to identify the availability and appropriateness of herbarium specimen images for comprehensive trait data extraction, we developed a spatio-temporal dataset on intraspecific trait variability containing 128,036 morphological leaf trait measurements for seven selected species.New informationAfter scrutinising the metadata of digitised herbarium specimen images available from iDigBio and GBIF (21.9 million and 31.6 million images for Tracheophyta; accessed date December 2020), we identified approximately 10 million images potentially appropriate for our study. From the 10 million images, we selected seven species (Salix bebbiana Sarg., Alnus incana (L.) Moench, Viola canina L., Salix glauca L., Chenopodium album L., Impatiens capensis Meerb. and Solanum dulcamara L.) , which have a simple leaf shape, are well represented in space and time and have high availability of specimens per species. We downloaded 17,383 images. Out of these, we discarded 5779 images due to quality issues. We used the remaining 11,604 images to measure the area, length, width and perimeter on 32,009 individual leaf blades using the semi-automated tool TraitEx. The resulting dataset contains 128,036 trait records.We demonstrate its comparability to trait data measured in natural environments following standard protocols by comparing trait values from the TRY database. We conclude that the herbarium specimens provide valuable information on leaf sizes. The dataset created in our study, by extracting leaf traits from the digitised herbarium specimen images of seven selected species, is a promising opportunity to improve ecological knowledge about the adaptation of size-related leaf traits to environmental changes in space and time.

Dataset Information

Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum).

Publications

Using computer vision on herbarium specimen images to discriminate among closely related horsetails (<i>Equisetum</i>).

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