Proving that cost-effective, data-driven solutions can rival expensive, high-tech monitoring systems in aquaculture disease management
AQUAROM rethinks technology investment in aquaculture—demonstrating that strategic resource use, rather than higher spending, drives innovation and productivity.
R&D spending has increased significantly, yet productivity gains have stalled. High-cost sensor networks show diminishing returns on disease prevention. We identify optimal sensor configurations and reduce operational costs while maintaining effectiveness.
Disease outbreaks including white spot syndrome and seasonal fish kills cause major losses. Farmers lack access to affordable monitoring tools and early warning systems. We provide validated, low-cost solutions tailored to resource-limited settings.
By conducting field trials in the Philippines and cross-validating with Norwegian data, we bridge the gap between resource-limited and high-tech aquaculture environments, creating practical solutions for both contexts.
Comprehensive framework optimizing monitoring strategies, predictive modeling, and stakeholder-driven decision-making
Identify cost-effective sensor configurations that eliminate redundancy while maintaining disease detection reliability across different aquaculture environments.
Create AI-driven models that detect disease outbreaks with limited data, achieving ≥80% accuracy in early warning systems using machine learning for sparse data environments.
Study how farmers interpret and apply model outputs to improve adoption rates and practical implementation, ensuring solutions are farmer-friendly.
Test whether low-cost Philippine solutions work effectively in Norway's high-tech operations and vice versa, proving global applicability.
Develop federated learning framework for multi-farm data sharing while protecting proprietary information and farmer privacy.
Train 3 PhD students, 2 MSc students, and engage policymakers and industry stakeholders to ensure knowledge transfer and sustainable impact.
AQUAROM challenges the assumption that higher spending automatically leads to better outcomes
Proves cost-effective solutions can rival expensive systems through optimized sensor configurations and intelligent data analysis.
Philippines serves as testbed for innovation; Norway validates and adopts successful approaches in high-tech operations.
Solutions co-created with farmers and companies ensuring practical applicability and real-world effectiveness.
Benefits both high-tech and resource-limited aquaculture operations worldwide through adaptable frameworks.
Engages regulators in Norway and Philippines to ensure alignment with governance frameworks and industry standards.
Committed to open-access data and algorithms following FAIR principles for maximum research impact.
Leading research institutions, government agencies, and industry partners across three countries
Project lead, AI/ML modeling, predictive models, and overall coordination
Industry data provider, Norwegian field validation lead
Fish epidemiology, environmental factor analysis, field coordination
Sensor optimization co-lead, Philippine field trials
Government agency, field trials lead, policy alignment
Aquaculture industry partner, field data provider
Sensor optimization co-lead, privacy-preserving ML development
Project kickoff, PhD recruitment, lab testing of sensor configurations
Predictive models development, Philippine field trials, data collection and analysis
Cross-validation with Norwegian data, final reporting, dissemination and publications
We welcome inquiries about research partnerships, PhD/MSc opportunities, industry collaboration, and stakeholder engagement.
• PhD positions (3 available)
• MSc student projects (2 available)
• Research collaborations
• Industry partnerships
• Stakeholder workshops