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Automated software for counting and measuring Hyalella genus using artificial intelligence.

Ludy Pineda-Alarcón1, Maycol Zuluaga2, Santiago Ruíz2

  • 1Environmental Management and Modeling Group (GAIA), Environmental School, Engineer Faculty, Universidad de Antioquia, Medellín, Colombia. ludy.pineda@udea.edu.co.

Environmental Science and Pollution Research International
|November 22, 2023
PubMed
Summary

HyACS software uses AI to rapidly and accurately count Hyalella amphipods and measure their morphology. This tool enhances bioindicator studies for aquatic ecosystem health assessments.

Keywords:
Deep learningImage captureMacroinvertebratesMeasuring protocolsMorphological traits

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Area of Science:

  • Ecotoxicology
  • Limnology
  • Aquatic Ecology

Background:

  • Hyalella amphipods are key macroinvertebrates in aquatic ecosystems.
  • Data on Hyalella populations and morphology are crucial for environmental assessments.
  • Current methods for counting and measuring Hyalella are time-consuming.

Purpose of the Study:

  • Introduce HyACS, a novel software tool for Hyalella analysis.
  • Automate the detection and morphological measurement of Hyalella.
  • Improve the efficiency and precision of ecological studies using Hyalella.

Main Methods:

  • Developed a detection model using YOLOv3 architecture.
  • Employed digital image processing techniques for morphological analysis.
  • Utilized basic imaging equipment for data capture.

Main Results:

  • HyACS software accurately identifies Hyalella individuals with >90% accuracy.
  • The tool provides precise measurements of body length, arc length, width, eccentricity, perimeter, and area.
  • HyACS operates up to four times faster than manual counting methods.

Conclusions:

  • HyACS offers a significant advancement in Hyalella population and morphological studies.
  • The software enhances the utility of Hyalella as bioindicators of water quality.
  • This tool can streamline ecological monitoring and research in aquatic environments.