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Related Concept Videos

Testing Water Quality01:14

Testing Water Quality

440
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
440

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Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
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Real-Time Pond Water Assessment via Embedded Deep Learning and Visual Data Acquisition: A Practical Monitoring

Prawit Chumchu1, Kailas Patil2, Alfa Nyandoro3

  • 1Kasetsart University; prawit@eng.src.ku.ac.th.

Journal of Visualized Experiments : Jove
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Summary
This summary is machine-generated.

This study introduces an autonomous robot for real-time water quality monitoring in aquaculture. Using underwater image classification, it accurately assesses water conditions, supporting sustainable fish farming.

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Area of Science:

  • Aquaculture
  • Environmental Monitoring
  • Artificial Intelligence

Background:

  • Optimal water quality is crucial for aquaculture, especially for ornamental species like Koi.
  • Aesthetics and health in Koi are directly tied to water clarity and environmental stability.
  • Current monitoring methods can be invasive or lack continuous assessment capabilities.

Purpose of the Study:

  • To develop and present a protocol for a real-time water quality monitoring system.
  • To utilize underwater image classification with embedded deep learning for autonomous assessment.
  • To provide a scalable and affordable solution for sustainable aquaculture practices.

Main Methods:

  • Integration of a low-cost underwater camera and Raspberry Pi controller in an autonomous robot.
  • Development of a lightweight neural network for classifying five water conditions (clear to turbid).
  • Deployment of the system for autonomous image capture, transmission, and analysis.

Main Results:

  • Achieved over 99% classification accuracy on a custom dataset for water conditions.
  • The system autonomously captures, transmits, and classifies aquatic images in real-time.
  • Demonstrated continuous assessment and visual alerts for deteriorating water quality.

Conclusions:

  • The developed system offers a non-invasive, continuous method for evaluating aquatic environments.
  • This approach supports sustainable aquaculture through affordable and scalable technology.
  • The visual monitoring protocol is adaptable to various aquaculture and environmental contexts.