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Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System
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AI-based seagrass morphology measurement.

Sajal Halder1, Nahina Islam2, Biplob Ray2

  • 1College of ICT, School of Engineering and Technology, Central Queensland University, Melbourne, Australia; Data61, CSIRO, Melbourne, Australia.

Journal of Environmental Management
|September 6, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed an AI model to automatically measure seagrass growth from images, improving accuracy and efficiency for conservation efforts in the Great Barrier Reef. This automated approach aids in understanding seagrass health and mitigating decline.

Keywords:
Deep learningImage classificationMorphometricsObject detectionSeagrassZostera muelleri

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Area of Science:

  • Marine biology
  • Ecology
  • Computational science

Background:

  • Seagrass meadows are vital to the Great Barrier Reef, offering ecological services like nutrient filtration, habitat provision, and blue carbon sequestration.
  • Understanding seagrass phenotypic plasticity is crucial for assessing ecosystem health and developing conservation strategies against environmental stressors.
  • Accurate measurement of seagrass morphological parameters (leaf, rhizome, root dimensions) is essential for growth assessment but traditionally time-consuming and labor-intensive.

Purpose of the Study:

  • To develop and evaluate an automated machine learning model for measuring seagrass morphological parameters from digital imagery.
  • To enhance the efficiency and accuracy of seagrass growth assessment, supporting conservation initiatives.
  • To provide a publicly available tool for seagrass research.

Main Methods:

  • Utilized a deep learning model, YOLO-v6, for image processing and artificial intelligence.
  • The model was trained to classify three distinct seagrass object types and determine their dimensions from digital images.
  • Performance was evaluated using metrics such as recall, precision, and F1 score.

Main Results:

  • The YOLO-v6 model demonstrated high effectiveness in measuring seagrass morphological parameters.
  • Achieved an average recall of 97.5%, average precision of 83.7%, and average F1 score of 90.1%.
  • The developed model code was made publicly available on GitHub.

Conclusions:

  • The AI-powered model significantly improves the automation of seagrass morphological measurements.
  • This technology offers a more accurate and efficient alternative to manual measurement, aiding in seagrass research and conservation.
  • The public availability of the model promotes wider adoption and advancement in seagrass monitoring.