L2M – AI-Powered Real-Time Detection: Revolutionizing Harmful Algae Bloom (HAB) Monitoring

The proposal introduces a cutting-edge, real-time solution for detecting and mitigating harmful algal blooms (HABs) using advanced computer vision and AI models integrated with autonomous systems. By leveraging state-of-the-art AI-powered imaging technology, this solution delivers precise species identification and real-time monitoring capabilities that drastically improve response times. Unlike traditional manual sampling methods, this system utilizes machine learning algorithms to process vast amounts of environmental data instantly, providing continuous insights and reducing the need for human intervention.
With its robust AI and computer vision framework, this solution holds immense market potential in the global aquaculture and environmental monitoring industries, setting a new standard for scalability and accuracy. Its real-time, automated capabilities offer a competitive advantage in protecting marine ecosystems. It is particularly significant for high-impact regions like British Columbia and Atlantic Canada, where the stakes are high for aquaculture and public health.

Faculty Supervisor:

Mohsin Jamil

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Clean Technology; Artificial Intelligence; Aquaculture and Fishing

University:

Memorial University of Newfoundland

Program: