ECOPAMPA: A new tool for automatic fish schools detection and assessment from echo data.

Area of Science:

• Marine Biology
• Acoustic Technology
• Software Engineering

Background:

• Accurate identification of aquatic organisms and abundance estimation using echo detection is a significant challenge for marine researchers.
• Existing methods often lack the automation required for efficient large-scale data processing.

Purpose of the Study:

• To present a software architecture for automated echo data processing for fish species identification.
• To develop and validate algorithms for detecting fish aggregations and calculating fish school descriptors.

Main Methods:

• Development of a software platform (ECOPAMPA) to read echosounder data.
• Implementation of digital processing algorithms for automatic detection of fish aggregations, surface/bottom lines, and fish school descriptor calculation.
• Comparative analysis of ECOPAMPA against Echoview software using experimental data.

Main Results:

• ECOPAMPA successfully processes echo data to extract energetic, morphometric, and bathymetric fish school descriptors.
• Automated algorithms for surface/bottom line detection and fish aggregation identification were developed.
• Comparative results demonstrate the efficacy of ECOPAMPA in marine species identification.

Conclusions:

• The ECOPAMPA software provides an effective and automated solution for fish species identification and abundance estimation.
• This system represents a significant advancement for Argentinean marine research capabilities.
• The developed algorithms and software architecture can be applied to various echosounder systems.