CASE STUDY 2 Technology innovation for production 1 AI-powered fish monitoring by Dominion Solutions (South Africa)55,56 Disease and environmental stress often cause sudden losses in aquaculture. Smallholder and mid-scale farmers struggle to detect early warning signs, such as abnormal fish behaviour or water quality deterioration. Dominion Solutions has created AquaBrain Net, an AI-powered system using IoT sensors and cameras to monitor fish movement, gill activity and water conditions in real time. The system analyses data to detect stress or disease indicators and alerts farmers via a mobile dashboard for quick response. In pilot trials across South Africa, AquaBrain Net reduced feed waste and mortality, helping farmers adjust aeration, isolate affected fish and stabilize water conditions. It is particularly effective for small-scale farms with limited diagnostic support. 2 Improved tilapia hatchery systems by WorldFish (Egypt and Bangladesh)57 Hatcheries in low- and middle-income countries often produce variable-quality seed due to limited broodstock selection and inconsistent rearing practices. WorldFish partnered with national hatchery networks to standardize breeding, fertilization and fry-rearing protocols. The programme provided training, standard operating procedures and performance monitoring systems. As a result, fingerling survival rates rose by 30-50% and growth variability declined. In Bangladesh, farmers using certified fingerlings achieved up to 40% higher yields than those relying on uncertified hatcheries. Innovation in processing Boosting yield and product quality through smarter transformation methods Processing is vital for preserving the economic and nutritional value of blue foods, yet inefficiencies continue to cause major losses globally. In some industrial settings, up to 40% of biomass is lost during trimming, while inconsistent quality control and short shelf lives limit profitability and market confidence. In Africa and other emerging regions, processing capacity is often limited or labour- intensive, further increasing loss and reducing access to premium markets. Innovations are helping improve yield, consistency and product quality through automation, precision tools and low-impact transformation techniques that reduce waste and increase value (see Case study 3). Processing is vital for preserving the economic and nutritional value of blue foods. CASE STUDY 3 Technology innovation for processing 1 AI-based fish grading by Maritech (Norway)58 Manual grading errors cause rejections, delays and consumer distrust, particularly where export quality standards are strict. Maritech’s automated grading system uses AI-driven cameras and sensors to evaluate fish by weight, fat content, colour and texture, while integrated traceability modules allow issue tracking along supply chains. The system increases grading accuracy, consistency and compliance with market standards while reducing labour costs and unlocking premium prices.2 Smart solar dryers by NutriFish project (Uganda)59,60 Traditional sun drying, common in Africa, causes nutrient loss, contamination and spoilage, reducing product safety and market potential. The NutriFish project introduced low-cost, solar-powered dryers with controlled airflow and temperature management for small-scale processors, often located near landing sites to minimize post-harvest loss. The innovation improved food safety and shelf life, reduced contamination and enabled production of high- quality dried fish suitable for formal markets, increasing incomes, especially among women-led processors. Investing in Blue Foods: Innovation and Partnerships for Impact 19

Investing in Blue Foods 2026