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Seaweed Growth Monitoring with a Low-Cost Vision-Based System.

Jeroen Gerlo1, Dennis G Kooijman2, Ivo W Wieling3

  • 1InViLab Research Group, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium.

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|November 25, 2023
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Summary
This summary is machine-generated.

This study presents an automated underwater seaweed growth monitoring system using RGB and stereo cameras. The method accurately measures seaweed size in vertical farms, even in low visibility conditions.

Keywords:
aquacultureimage segmentationseaweed monitoringunderwater stereo imaging

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Area of Science:

  • Marine biology
  • Agricultural technology
  • Computer vision

Background:

  • Current seaweed monitoring relies on manual or lab-based methods.
  • Underwater monitoring of vertical seaweed farms is challenging due to visibility.
  • Automated systems are needed for efficient and scalable seaweed cultivation.

Purpose of the Study:

  • To develop and validate an automated underwater system for monitoring seaweed growth.
  • To assess the feasibility of using low-cost RGB and stereo vision for this purpose.
  • To quantify seaweed size in situ within a vertical farm setting.

Main Methods:

  • Combined low-cost RGB and stereo vision cameras for underwater imaging.
  • Employed deep learning (DeeplabV3+) for pixel-based seaweed segmentation.
  • Utilized stereo vision data to convert pixel measurements to real-world size (m²).

Main Results:

  • Achieved high segmentation accuracy (IoU of 0.9) despite poor underwater visibility.
  • Demonstrated a repeatability of 6% for seaweed size measurements.
  • Reported a precision of 18% for the determined seaweed size.

Conclusions:

  • The developed system offers a feasible solution for automated underwater seaweed growth monitoring.
  • The method shows promise for improving efficiency and scalability in seaweed farming.
  • Further refinement could enhance precision in challenging aquatic environments.