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Herbariograph: a deep-learning tool to classify specimen images.

Fabio Andrés Ávila^1,2, John Y Park^1,3, Leanna Feder¹

¹New York Botanical Garden, New York City, NY, 10458, USA.

The New Phytologist

|August 12, 2025

Summary

This summary is machine-generated.

Herbarium specimen images are crucial for plant diversity research. A new deep-learning model, Herbariograph, automates image sorting, improving data accessibility for scientific study.

Keywords:

Artificial Intelligence biodiversity herbarium specimen image classification images machine learning

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Area of Science:

Botany
Computer Science
Data Science

Background:

Herbarium specimens represent global plant diversity and are vital research resources.
While millions of herbarium specimens exist, only about 12.8% have associated digital images, limiting research potential.
Automated sorting of specimen images is essential for unlocking the wealth of information they contain.

Purpose of the Study:

To develop an automated method for recognizing and categorizing herbarium specimen image types.
To introduce Herbariograph, an open dataset and deep-learning model for image classification in herbaria.

Main Methods:

Creation of the Herbariograph dataset, comprising 17 categories with 12,288 images each, sourced from 43 institutions.
Training a Convolutional Neural Network (CNN) model on the Herbariograph dataset for image recognition.
Evaluation of the model's performance using a test macro F1 score.

Main Results:

The Herbariograph dataset contains a diverse range of 17 image categories.
The trained CNN model achieved a high test macro F1 score of 0.9611.
The model demonstrates strong capability in automatically recognizing various herbarium image types.

Conclusions:

Herbariograph provides a valuable resource for automating the processing of herbarium specimen images.
This automation facilitates faster, more efficient, and accessible creation of targeted datasets for plant science research.
The developed deep-learning model enhances the utility of digital herbarium collections for scientific discovery.