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LeTra: a leaf tracking workflow based on convolutional neural networks and intersection over union.

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Plant Methods
|January 17, 2024
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Summary
This summary is machine-generated.

This study introduces an automated method for tracking individual plant leaves using Mask R-CNN, improving photosynthetic trait analysis in high-throughput phenotyping. The approach achieves high accuracy in leaf detection and tracking, essential for plant science research.

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Area of Science:

  • Plant Biology
  • Computational Biology
  • Agricultural Science

Background:

  • Plant photosynthesis is crucial for crop productivity and yield.
  • High-throughput phenotyping (HTP) facilities use chlorophyll fluorescence imaging for reliable photosynthetic trait measurement.
  • Automated leaf-level analysis in HTP is limited by manual annotation, necessitating automated leaf tracking.

Purpose of the Study:

  • To develop and validate an automated method for leaf segmentation and tracking in top-down plant images.
  • To provide datasets and code to facilitate community adoption and expansion of leaf tracking methodologies.
  • To address the need for efficient, automated leaf tracking in HTP platforms.

Main Methods:

  • Fine-tuning a Mask R-CNN model for leaf segmentation and detection.
  • Utilizing intersection over union (IoU) for evaluating segmentation accuracy.
  • Implementing a tracking algorithm tested on Arabidopsis thaliana plants.

Main Results:

  • Achieved a mean F-score of 0.956 for detection and 0.844 for segmentation overlap (IoU).
  • Successfully tracked 84.29% of leaves with a Higher Order Tracking Accuracy (HOTA) of 0.846.
  • Demonstrated that leaf age and order influence photosynthetic capacity and response to light treatments.

Conclusions:

  • The proposed method offers robust leaf tracking for top-down plant images.
  • The fine-tuning approach requires minimal training data for effective results.
  • Expanding the training dataset can further resolve tracking issues.