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An interactive AI-driven platform for fish age reading.

Arjay Cayetano¹, Christoph Stransky¹, Andreas Birk²

¹Thünen Institute of Sea Fisheries, Bremerhaven, Germany.

|November 18, 2024

Summary

This summary is machine-generated.

This study introduces an interactive website for explainable artificial intelligence (AI) in fish aging, enhancing accuracy and reducing bias in stock assessments. The platform integrates advanced machine learning techniques for improved fish population dynamics analysis.

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Area of Science:

Fisheries Science
Computational Biology
Artificial Intelligence

Background:

Accurate fish age estimation is crucial for fisheries stock assessment and understanding population dynamics.
Traditional age determination relies on expert interpretation of calcified structures, which can be subjective and time-consuming.
Artificial intelligence (AI) offers automated solutions but often lacks transparency, raising concerns about reliability.

Purpose of the Study:

To develop an interactive platform integrating explainable AI methods for fish age estimation.
To involve human experts directly in the AI training and development process.
To explore the application of advanced machine learning concepts for enhanced accuracy.

Main Methods:

Development of an interactive website featuring explainable AI models (U-Net, Mask R-CNN).
Integration of machine learning techniques including transfer learning, ensemble learning, and continual learning.
Direct user involvement for AI model training and refinement.

Main Results:

The developed platform successfully incorporates explainable AI for fish aging.
Advanced machine learning techniques demonstrated effectiveness in improving age estimation accuracy.
User involvement in the AI development loop was facilitated.

Conclusions:

The interactive website provides a transparent and collaborative approach to AI-driven fish aging.
Explainable AI, combined with advanced ML, offers a robust solution for automated and reliable fish stock assessment.
This approach minimizes human bias and enhances the efficiency of age determination in fisheries research.