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Related Experiment Video

Updated: May 9, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Enhancing plant morphological trait identification in herbarium collections through deep learning-based segmentation.

Hanane Ariouat1, Youcef Sklab1, Edi Prifti1,2

  • 1Institut de Recherche pour le Développement (IRD) Sorbonne Université UMMISCO, F-93143, Bondy France.

Applications in Plant Sciences
|May 1, 2025
PubMed
Summary

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A gut microbiome-kidney-heart axis predictive of future cardiovascular diseases.

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Removing background elements from herbarium scans using deep learning image segmentation improves plant trait identification. This method enhances classification accuracy and F1 scores for biodiversity research.

Area of Science:

  • Botany
  • Computer Science
  • Biodiversity Research

Background:

  • Digitized herbarium collections are crucial for plant evolution and biodiversity studies.
  • Analyzing these collections with deep learning is challenging due to heterogeneous backgrounds.
  • Removing non-plant backgrounds is hypothesized to improve algorithm performance.

Purpose of the Study:

  • To develop a deep learning method for segmenting plant masks and removing backgrounds from herbarium scans.
  • To improve the accuracy of plant trait identification using processed herbarium images.

Main Methods:

  • A novel deep learning approach was used to segment plant masks and remove non-plant backgrounds.
  • Semi-automatic preprocessing reduced manual effort in dataset preparation.
Keywords:
deep learningherbarium scanssemantic segmentationtrait classification

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  • The method was evaluated on its effectiveness in image segmentation and subsequent classification tasks.

    • Main Results:

    • Image segmentation achieved a high F1 score of up to 96.6%.
    • Segmentation improved classification accuracy by up to 3% and F1 score by up to 7% for plant trait identification.
    • The processed images significantly enhanced the performance of classification models.

    Conclusions:

    • Effective image segmentation is vital for analyzing herbarium scans.
    • The proposed method successfully isolates plant elements, improving downstream classification tasks.
    • This approach enhances the utility of digitized herbarium collections for scientific research.