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Updated: Jun 20, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

A deep learning-based automated Solar-Powered Fish Monitoring System.

Emmanuel Ahene¹, Richmond Owusu Agyei¹, Rose-Mary Owusuaa Mensah Gyening¹

¹Department of Computer Science, Kwame Nkrumah University of Science and Technology PMB, UPO, Kumasi, Ghana.

|June 18, 2026

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This study introduces a cost-effective, solar-powered automation system for green fish farming. It uses AI to monitor fish health and environment, reducing labor and improving sustainability.

Area of Science:

Aquaculture and Environmental Science
Artificial Intelligence and Computer Vision
Renewable Energy Systems

Background:

Current green fish farming is labor-intensive and costly due to grid energy reliance.
Operational inefficiencies and high fish mortality are significant challenges in existing practices.

Purpose of the Study:

To develop a cost-effective, solar-powered automation system for green fish farming.
To integrate computer vision and deep learning for real-time monitoring and management.
To enhance production efficiency and environmental sustainability in aquaculture.

Main Methods:

Designed a modular and scalable system architecture for automation.
Developed a smart system using computer vision and deep learning (CNNs) with custom datasets.

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Automated Measurements of Sleep and Locomotor Activity in Mexican Cavefish

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Last Updated: Jun 20, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

Automated Measurements of Sleep and Locomotor Activity in Mexican Cavefish

Automated Measurements of Sleep and Locomotor Activity in Mexican Cavefish

Published on: March 21, 2019

Integrated a renewable energy subsystem with photovoltaic panels and battery storage.

Utilized low-cost sensors and open-source software for economic viability.

Main Results:

Achieved accurate real-time monitoring of fish behavior, water quality, feeding, and waste management.
Enabled early disease detection through camera feeds and precise feeding control.
Demonstrated significant improvements in monitoring accuracy and reduced manual intervention via simulations.
Confirmed enhanced operational sustainability and economic viability for farmers.

Conclusions:

The integrated solar-powered automation system effectively addresses key challenges in green fish farming.
The multidisciplinary approach offers a scalable and cost-effective solution for various farm sizes.
This technology promotes greater efficiency, sustainability, and economic viability in aquaculture.