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Updated: Aug 25, 2025

Author Spotlight: Advancing Coral Culture - Creating a Semi-Quantitatively Controlled Microenvironment System to Counter Current Limitations

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A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology.

Chin-Chun Chang¹, Naomi A Ubina^1,2, Shyi-Chyi Cheng¹

¹Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202, Taiwan.

Sensors (Basel, Switzerland)

|October 14, 2022

Summary

This summary is machine-generated.

This study introduces a smart underwater camera system for precision aquaculture. The AI-powered device accurately monitors fish quantity and size, even in challenging conditions, improving fish farming efficiency.

Keywords:

Mask R-CNN convolutional neural networks gaussian mixture models object detection CNN semantic segmentation networks sonar images stereo RGB images

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Area of Science:

Aquaculture Technology
Underwater Imaging
Artificial Intelligence in Fisheries

Background:

Precision aquaculture requires non-intrusive monitoring of fish in various water conditions.
Traditional methods struggle with turbid or low-light environments and crowded fish schools.

Purpose of the Study:

To develop and validate a two-mode underwater surveillance camera system for monitoring fish in aquaculture.
To enhance fish quantity and size estimation using AI in challenging underwater environments.

Main Methods:

Development of a two-mode camera system integrating sonar and stereo RGB imaging.
Application of machine learning (Mask R-CNN, CNNs, semantic segmentation) for sonar image analysis.
3D spatial alignment of sonar and RGB data for AI-driven fish annotation.
Cloud-based AIoT system for data collection and analysis in aquaculture tanks and net cages.

Main Results:

AI functions accurately estimate fish quantity and size distribution from sonar images.
Overlapping fish instances in crowded schools were effectively analyzed using machine learning.
Integrated sonar and RGB data improved fish annotation accuracy.
The system demonstrated feasibility and suitability for remote underwater fish metric estimation.

Conclusions:

The developed smart camera system offers a robust solution for non-intrusive fish monitoring in aquaculture.
AI integration significantly improves the accuracy of fish stock assessment in challenging underwater conditions.
This technology supports precision aquaculture by providing reliable, remote sensing of fish populations.