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Related Experiment Video

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Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
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Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models.

Naruephorn Tengtrairat1, Wai Lok Woo2, Phetcharat Parathai1

  • 1School of Software Engineering, Payap University, Chiang Mai 50000, Thailand.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a 2D computer vision technique for non-intrusively estimating Tilapia fish weight in turbid waters. The method uses a single low-cost camera, offering a practical alternative to expensive stereo systems for aquaculture monitoring.

Keywords:
aquaculturedeep learningmachine visionnon-intrusive methodsweight estimation

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Area of Science:

  • Aquaculture technology
  • Computer vision
  • Biometrics

Background:

  • Efficient fish management, including feeding and harvesting, is crucial for sustainable aquaculture.
  • Accurate fish weight estimation is challenging in turbid underwater environments.
  • Existing methods often rely on costly equipment like stereo cameras.

Purpose of the Study:

  • To develop a non-intrusive, low-cost 2D computer vision method for estimating Tilapia fish weight.
  • To address the challenges of monitoring fish in turbid water conditions.
  • To provide a practical alternative to expensive underwater monitoring systems.

Main Methods:

  • Utilized a Mask Recurrent-Convolutional Neural Network (Mask R-CNN) for fish detection and pixel dimension extraction.
  • Estimated fish depth and converted pixel dimensions to centimeters.
  • Employed regression learning models (linear regression, random forest, support vector regression) for weight estimation.

Main Results:

  • Achieved a Mean Absolute Error (MAE) of 42.54 g for weight estimation.
  • Obtained an R-squared (R2) value of 0.70, indicating good model fit.
  • Reported an average weight error of 30.30 (±23.09) grams in turbid water.

Conclusions:

  • The proposed 2D computer vision framework offers a practical and cost-effective solution for underwater Tilapia weight estimation.
  • The method demonstrates feasibility in challenging turbid water environments.
  • This technology can support efficient aquaculture management through improved fish monitoring.