ArticleApplications in plant sciences2020
Applying machine learning to investigate long-term insect-plant interactions preserved on digitized herbarium specimens.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- An AI-powered smart Agribot for detecting locusts in farmlands using IoT and deep learning.Scientific reports · 2025Article
- Article
- Envisaging a global infrastructure to exploit the potential of digitised collections.Biodiversity data journal · 2023Article
- Tracing the invasion of a leaf-mining moth in the Palearctic through DNA barcoding of historical herbaria.Scientific reports · 2022Article
- Deep learning and computer vision will transform entomology.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Using Convolutional Neural Networks to Efficiently Extract Immense Phenological Data From Community Science Images.Frontiers in plant science · 2021Article
- A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction.Applications in plant sciences · 2020Article
- Applying machine learning to investigate long-term insect-plant interactions preserved on digitized herbarium specimens.Applications in plant sciences · 2020Article
- From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2.Applications in plant sciencesArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseDespite 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.
methodsWe 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 (
resultsDamage 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.
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