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Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Published on: October 2, 2016

Using reflectance spectra and Pl@ntNet to identify herbarium specimens: a case study with Lithocarpus.

Barbara M Neto-Bradley¹, Pierre Bonnet², Hervé Goëau²

¹Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, CB23EA, UK.

The New Phytologist

|June 5, 2025

Summary

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This summary is machine-generated.

Digitizing plant collections can improve data access. Leaf reflectance spectra and computer vision offer new ways to identify plant species, helping to fill data gaps in herbaria.

Area of Science:

Botany
Taxonomy
Digital Herbariology

Background:

Digitization of plant collections is increasing data accessibility.
Traditional taxonomic identification in collections has declined, leading to more specimens identified only to family or genus.
This risks widening the data gap for understudied species.

Purpose of the Study:

To compare the effectiveness of hyperspectral reflectance and computer vision for herbarium-based plant species identification.
To assess the performance of spectral data for identifying Lithocarpus species, considering data volume, discrimination ability, and accuracy with close relatives.
To evaluate Pl@ntNet (a computer vision approach) against spectral data for specimen identification.

Main Methods:

Used Lithocarpus species as a case study.

Keywords:

Lithocarpus Pl@ntNet herbarium specimens indeterminate specimens leaf reflectance spectra species identification

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Last Updated: Jun 20, 2026

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

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Published on: October 2, 2016

Field Identification of Matricaria chamomilla using a Portable qPCR System

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Compared species classification accuracy using leaf reflectance spectra versus computer vision (Pl@ntNet).

Assessed spectral data requirements for optimal classification, species discrimination, and identification of closely related taxa.

Main Results:

Lithocarpus herbarium specimens were accurately identified to species using limited spectral datasets.
Spectral identification accuracy was only 14% lower than Pl@ntNet, despite not using reproductive structures.
Close relatives were more frequently confounded in spectral identification.

Conclusions:

Rapid, non-destructive leaf reflectance measurements show promise for plant identification in herbaria.
Hyperspectral reflectance, combined with computer vision, can help fill identification gaps, especially for specimens lacking reproductive features.
This approach can complement existing methods to improve the completeness of digitized plant collection data.