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Updated: Jun 6, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

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Published on: February 25, 2021

Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning.

Shenlan Zhang^1,2,3, Shaojie Wu^1,2, Liqiang Chen^1,2

¹Key Laboratory of Advanced Manufacturing and Automation Technology, Education Department of Guangxi Zhuang Autonomous Region, Guilin University of Technology, Guilin 541006, China.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

This study introduces a new deep learning method for multi-target water quality colorimetric detection, improving accuracy and efficiency for on-site testing. The optimized model achieves high precision and recall, outperforming existing approaches for rapid water analysis.

Keywords:

colorimetric sensor deep learning water quality detection

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Area of Science:

Environmental Science
Analytical Chemistry
Computer Science

Background:

Colorimetric methods offer rapid, low-cost on-site water quality testing.
Existing deep learning for colorimetric detection primarily focuses on single-target classification.
There is a need for automated, multi-target water quality analysis.

Purpose of the Study:

To develop a multi-task water quality colorimetric detection method using deep learning.
To automate the process of water quality assessment from image input to result output.
To enhance detection accuracy while reducing computational load for on-site applications.

Main Methods:

A multi-task water quality colorimetric detection method based on YOLOv8n was proposed.
A dataset of colorimetric sensor data under varied lighting conditions was constructed.
Improvements included MGFF, LSKA-SPPF, and GNDCDH modules for enhanced deep learning performance.

Main Results:

The optimized algorithm achieved high precision (96.4%), recall (96.2%), and mAP50 (98.3%).
Parameter count and computational load were reduced by 25.8% and 25.6% compared to YOLOv8n.
Significant improvements in precision, recall, mAP50, and mAP95 were observed.

Conclusions:

The proposed method demonstrates substantial potential for rapid on-site water quality detection.
The optimized deep learning approach offers new technological insights for water quality monitoring.
This work advances automated, multi-target analysis in environmental sensing.