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Enabling automated herbarium sheet image post-processing using neural network models for color reference chart

Dakila A Ledesma1, Caleb A Powell2, Joey Shaw2

  • 1Department of Computer Science and Engineering University of Tennessee at Chattanooga Chattanooga Tennessee USA.

Applications in Plant Sciences
|March 19, 2020
PubMed
Summary
This summary is machine-generated.

New neural networks automatically detect color reference charts (CRCs) in herbarium images, improving digitization accuracy and speed. This advancement aids in automating post-processing tasks for biological specimens.

Keywords:
automationdigitizationherbariummachine learningnatural history collectionsspecimen images

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Area of Science:

  • Botany
  • Computer Science
  • Digital Imaging

Background:

  • Digitizing herbaria yields millions of plant images (e.g., iDigBio).
  • Automating image post-processing saves time and enhances data quality in specimen digitization.

Purpose of the Study:

  • Develop and validate neural network methodologies for automatic color reference chart (CRC) detection.
  • Enable future automation of herbarium image post-processing tasks.

Main Methods:

  • Developed a novel neural network model, ColorNet, for CRC identification.
  • Tested ColorNet on 1000 herbarium specimen images from 52 herbaria.
  • Proposed modifications to Faster R-CNN for improved inference speed with larger CRCs.

Main Results:

  • ColorNet achieved a 30% accuracy increase for detecting CRCs smaller than 4 cm² compared to state-of-the-art models.
  • The developed neural networks effectively detect various CRC sizes.

Conclusions:

  • The proposed neural networks can automate herbarium digitization workflows.
  • Potential applications include automated image orientation and white balance correction.