Applying machine learning to investigate long-term insect-plant interactions preserved on digitized herbarium specimens.

ABSTRACT:

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

SUBMITTER: Meineke EK 

PROVIDER: S-EPMC7328658 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature