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Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

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Published on: March 8, 2018

Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research.

Katelin D Pearson¹, Gil Nelson², Myla F J Aronson³

¹California Polytechnic State University, San Luis Obispo, California.

|July 16, 2020

Summary

Machine learning (ML) can extract plant phenology data from herbarium specimen images. This approach aids in understanding climate change impacts and plant life cycles.

Keywords:

biodiversity climate change deep learning machine learning phenology

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Area of Science:

Botany
Ecology
Computer Science

Background:

Herbarium specimens offer a rich, historical dataset for plant research.
Plant phenology, crucial for understanding climate change, has been underutilized in digital data extraction.
Machine learning (ML) techniques are increasingly applicable to scientific discovery.

Purpose of the Study:

To present a generalized, modular ML workflow for extracting phenological data from herbarium specimen images.
To discuss the benefits and limitations of ML-driven phenological data extraction.
To highlight the potential of ML in advancing plant science and ecological research.

Main Methods:

Development of a generalized, modular machine learning workflow.
Application of ML techniques to analyze images of herbarium specimens.
Extraction of plant phenological data from digitized collections.

Main Results:

A functional ML workflow for extracting phenological data from herbarium images was described.
The study outlines the advantages and limitations of this novel approach.
Potential for future improvements and broader data aggregation was discussed.

Conclusions:

ML offers a powerful tool for unlocking historical data in herbarium collections.
Specimen-based ML methods can significantly enhance our understanding of plant phenology and climate change.
Investment in these methods and data aggregation is crucial for advancing Earth science.