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Related Concept Videos

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens.

William N Weaver1,2, Julienne Ng1, Robert G Laport3

  • 1Department of Ecology and Evolutionary Biology University of Colorado Boulder Boulder Colorado 80309 USA.

Applications in Plant Sciences
|July 7, 2020
PubMed
Summary
This summary is machine-generated.

LeafMachine autonomously measures plant leaves from herbarium specimens using machine learning, significantly increasing trait data availability for evolutionary and ecological studies.

Keywords:
LeafMachinecomputer visionherbarium digitizationleaf morphologymachine learning

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Area of Science:

  • Botany
  • Computational Biology
  • Ecology

Background:

  • Herbarium specimens offer valuable phenotypic data for plant evolution and ecology.
  • Manual measurement of these traits is labor-intensive and time-consuming.

Purpose of the Study:

  • To introduce LeafMachine, an automated application for measuring leaf traits from digitized herbarium specimens.
  • To leverage machine learning for efficient and scalable phenotypic data extraction.

Main Methods:

  • LeafMachine employs an ensemble of machine learning algorithms to analyze digitized leaf images.
  • The application was trained on 2685 specimens from 138 herbaria.
  • Performance was evaluated on specimens from 20 diverse plant families with varying image quality.

Main Results:

  • LeafMachine successfully extracted measurements from 82.0% of high-resolution and 60.8% of low-resolution images.
  • A very low percentage of specimens with visually measurable leaves were not measured (0.9% high-res, 2.1% low-res).

Conclusions:

  • LeafMachine offers a flexible, autonomous solution to automate leaf trait measurement.
  • This tool can substantially enhance the volume of trait data from herbarium collections.
  • The increased data accessibility will support numerous evolutionary and ecological research endeavors.