A Low-Cost pH Sensor for Real-Time Monitoring of Aquaculture Systems in a Multi-Layer Wireless Sensor Network

Area of Science:

• Environmental Science
• Sensor Technology
• Aquaculture Engineering

Background:

• pH is a critical water quality indicator in aquaculture, directly impacting aquatic life and related parameters like ammonia.
• Existing chemical reference pH sensors require frequent calibration and are costly, limiting their use in continuous in situ monitoring.
• There is a need for cost-effective, low-maintenance sensors for real-time aquaculture water quality assessment.

Purpose of the Study:

• To develop a novel, low-cost pH sensor with temperature compensation for aquaculture systems.
• To implement a machine learning algorithm for electronic calibration, reducing reliance on traditional chemical methods.
• To enhance sensor performance in terms of sensitivity and response time for improved in situ monitoring.

Main Methods:

• Development of a novel pH sensor incorporating temperature compensation.
• Implementation of a machine learning (ML) algorithm for electronic calibration and data processing.
• System design for a multi-node, master-controlled sensor network suitable for aquaculture environments.

Main Results:

• The developed sensor demonstrated improved sensitivity, increasing from 0.288 µA/pH to 0.316 µA/pH.
• Response time was significantly enhanced, reducing from 1 second to 125 milliseconds.
• Electronic calibration eliminated the need for frequent chemical calibration and reduced maintenance.

Conclusions:

• The novel ML-based pH sensor is a cost-effective and efficient solution for real-time, in situ water quality monitoring in aquaculture.
• The sensor's enhanced performance characteristics make it suitable for continuous monitoring in complex aquaculture environments.
• This technology offers a viable alternative to traditional pH sensors, addressing limitations of cost and maintenance.