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Automated Secchi disk depth measurement based on artificial intelligence object recognition.

Harshit Khanna1, Y W Fan2, S N Chan2

  • 1Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.

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|November 26, 2022
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Summary
This summary is machine-generated.

Researchers developed an AI algorithm to automatically measure water transparency using Secchi disk (SD) images. This method accurately mimics manual measurements, enabling high-frequency monitoring of water quality and prediction of events like algal blooms.

Keywords:
Artificial intelligenceObject detectionRemote sensingSecchi disk depthWater qualityWater transparency

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Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Artificial Intelligence

Background:

  • Water transparency is crucial for aquatic ecosystems and is typically measured using the Secchi disk (SD).
  • Manual Secchi disk depth (SDD) measurements are labor-intensive, limiting detailed analysis of water transparency dynamics.
  • Understanding diurnal and daily variations in water transparency is essential for effective water quality management.

Purpose of the Study:

  • To develop and validate an AI-based object detection algorithm for automatic Secchi disk (SD) detection.
  • To automate the measurement of Secchi disk depth (SDD) from images, mimicking human visual assessment.
  • To enable high-frequency water transparency monitoring for improved water quality insights.

Main Methods:

  • An artificial intelligence object detection algorithm was trained to identify the Secchi disk in images.
  • The algorithm was validated using approximately 2000 images captured daily from a remote-controlled imaging system.
  • The study was conducted in a fish farm located in a Hong Kong embayment.

Main Results:

  • The AI model demonstrated high accuracy in detecting the Secchi disk, achieving 93% detection accuracy.
  • The automated method successfully mimicked the visual Secchi disk depth (SDD) measurement process.
  • The system provided a high frequency of water transparency data, overcoming limitations of manual measurements.

Conclusions:

  • The AI-driven approach offers a novel, automated method for measuring water transparency via Secchi disk images.
  • This technology facilitates continuous, high-frequency monitoring, crucial for understanding short-term water quality fluctuations.
  • The findings open avenues for nowcasting and forecasting water quality changes, such as algal blooms, in coastal environments.