Related Experiment Video
Updated: Dec 16, 2025
Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens
Published on: March 8, 2018
Applying machine learning to investigate long-term insect-plant interactions preserved on digitized herbarium
Emily K Meineke1, Carlo Tomasi2, Song Yuan3
1Department of Entomology and Nematology University of California Davis California 95616 USA.
Premise:
Despite the economic significance of insect damage to plants (i.e., herbivory), long-term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be used to collect big data on plant-insect interactions during the Anthropocene.
Methods:
We initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens by training an insect damage detector and a damage type classifier on two distantly related plant species (Quercus bicolor and Onoclea sensibilis). We experimented with (1) classifying six types of herbivory and two control categories of undamaged leaf, and (2) detecting two of the damage categories for which several hundred annotations were available.
Results:
Damage detection results were mixed, with a mean average precision of 45% in the simultaneous detection and classification of two types of damage. However, damage classification on hand-drawn boxes identified the correct type of herbivory 81.5% of the time in eight categories. The damage classifier was accurate for categories with 100 or more test samples.
Discussion:
These tools are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models, allowing researchers to extract similar data and apply them to biological hypotheses.
