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  1. Home
  3. A User-friendly Machine Learning Pipeline For Automated Leaf Segmentation In Atriplex Lentiformis.
  1. Home
  3. A User-friendly Machine Learning Pipeline For Automated Leaf Segmentation In Atriplex Lentiformis.

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A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation in Atriplex lentiformis.

Michelle Lynn Yung1,2, Kamila Murawska-Wlodarczyk1,3, Alicja Babst-Kostecka1,3

  • 1Superfund Research Program, The University of Arizona, Tucson, AZ, USA.

Bioinformatics and Biology Insights
|June 11, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We developed an automated leaf segmentation pipeline for plant phenotyping using deep learning. Our specialized Mask R-CNN model outperforms large foundation models for this specific task, offering a scalable and user-friendly solution.

Keywords:
Atriplex lentiformisGrounding DINOLeaf segmentationMask R-CNNQR code detectionSegment Anything Modeldeep learningplant phenotypingtransfer learning

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Area of Science:

  • Plant Science
  • Computational Biology
  • Agricultural Technology

Background:

  • Automated leaf segmentation is crucial for plant phenotyping but requires balancing accuracy, scalability, and usability.
  • Existing deep learning models may not be optimized for specialized plant research datasets.

Purpose of the Study:

  • To develop and evaluate an end-to-end deep learning pipeline for practical plant phenotyping.
  • To compare a fine-tuned Mask R-CNN model against foundation models like Segment Anything Model (SAM).
  • To integrate automated sample identification and create a user-friendly application for researchers.

Main Methods:

  • Developed a pipeline using a fine-tuned Mask R-CNN segmentation model trained on 176 plant images.
  • Compared Mask R-CNN performance against Meta AI's SAM, utilizing Grounded SAM and Leaf-Only SAM post-processing.
  • Integrated QR codes for sample identification and benchmarked decoding libraries.
  • Deployed the pipeline as a Streamlit web application.

    • Main Results:

    • The fine-tuned Mask R-CNN achieved a high Dice coefficient (0.781) despite a small training dataset.
    • Transfer learning on a specialized dataset demonstrated superior performance over a large foundation model for domain-specific tasks.
    • QR code integration and decoding proved robust under various real-world imaging conditions.

    Conclusions:

    • Specialized, fine-tuned deep learning models can outperform general foundation models in specific scientific domains like plant phenotyping.
    • The developed pipeline offers an open-source, scalable, and user-friendly framework for plant research.
    • Addressing practical deployment challenges enhances the usability and adoption of automated tools by researchers.