Comprehensive framework optimizing monitoring strategies, predictive modeling, and stakeholder-driven decision-making
Six integrated work packages combining sensor optimization, fish epidemiology, AI-driven predictive models, stakeholder co-creation, and knowledge transfer
Will identify cost-effective sensor configurations through lab-scale benchmarking and comparative pen trials. Utilize Ensemble Kalman Filtering (EnKF) to enhance sensor robustness and cross-correct readings. Validate streamlined sensor combinations that eliminate redundancy while maintaining disease detection reliability across different aquaculture environments.
Will integrate historical disease records with real-time sensor data to develop epidemiological models linking environmental and biological factors to disease outbreaks. Create AI-driven predictive models using mixup augmentation and generative modeling to handle sparse data, achieving ≥80% accuracy in early warning systems. Apply privacy-preserving federated learning for secure multi-farm data sharing.
Will conduct structured field studies across 6-10 Philippine aquaculture sites to analyze how farmers interpret predictive model outputs and integrate them into decision-making. Organize co-creation workshops with farmers, industry experts, and policymakers to refine user interfaces, ensure practical applicability, and improve adoption rates through iterative feedback loops.
Will conduct data-driven cross-validation using Norwegian aquaculture datasets from Manolin AS (accessing 1/3 of Norwegian data). Down-sample high-resolution data to simulate minimal-sensor setups, assessing whether AI-driven models can compensate for fewer sensors. Develop best-practice guidelines for adapting cost-effective Philippine solutions to Norway's high-precision aquaculture and vice versa.
Will develop federated learning and differential privacy frameworks for secure multi-farm data consolidation without compromising business confidentiality. Enable collaborative learning where farms contribute to improved predictive models while protecting proprietary operational data and competitive information.
Will train 3 PhD students (sensor optimization, fish epidemiology, AI-driven disease prediction) and 2 MSc students in aquaculture technology and data analytics. Publish 8+ peer-reviewed articles, organize 3 annual stakeholder workshops (Norway, Philippines, Indonesia), conduct industry seminars, develop policy briefs, and engage regulators to maximize project impact and ensure sustainable knowledge transfer.
Combining lab-scale testing, field trials, AI-driven modeling, and cross-validation to develop practical, cost-effective solutions
Benchmark sensor configurations under controlled conditions before field deployment. Test sensitivity and stability across varying salinity, pH, temperature, and dissolved oxygen levels to validate minimal-sensor setups against high-end alternatives.
Two seasonal field trials (3-4 months each) across 6-10 sites in collaboration with BFAR, HOC PO Feed Corp, and local aquaculture operators. Real-world validation of low-cost monitoring solutions and stakeholder co-creation to ensure practical applicability.
Data-driven cross-validation using Manolin AS datasets (1/3 of Norwegian aquaculture data). Down-sample high-resolution data to simulate minimal-sensor setups, assessing whether AI-driven models can compensate for fewer sensors while maintaining prediction accuracy.
Mixup augmentation generates synthetic sensor readings to strengthen machine learning accuracy in data-sparse environments. Generative models simulate environmental stressors and disease outbreak scenarios to refine early-warning thresholds and improve robustness.
Federated learning and differential privacy techniques enable multi-farm data sharing for collaborative model training without exposing individual farm data. Protects business confidentiality while improving collective disease prediction capabilities.
Stakeholder workshops, focus groups, and iterative feedback sessions with farmers, industry experts, and policymakers ensure solutions align with real-world operations. User-centered design improves adoption rates and practical effectiveness.
AQUAROM challenges the assumption that higher spending automatically leads to better outcomes
Will prove 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.
Will engage 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.
Launch, PhD recruitment, organizational establishment, and initial lab testing
Initial predictive models deployed, AI prototypes ready for testing
Philippine field deployment across 6-10 sites (3-4 months)
Validation, iterative improvements, Norwegian cross-validation begins
Norwegian back-testing completed, comparative analysis finalized
Final reports, guidelines, policy briefs, and 8+ publications