The report investigates the potential of using underwater RGB imagery and computer vision techniques for in situ biomass estimation of cultivated kelp, specifically Saccharina latissima and Alaria esculenta. Key findings indicate that image quality is affected by phytoplankton blooms and turbidity, but meaningful size information can still be derived. The study demonstrates a strong relationship between computer vision-derived area estimates and actual biomass, suggesting that automated RGB imagery could simplify and improve biomass monitoring. Future research should focus on developing models for accurate farm-scale biomass monitoring using autonomous data collection and machine learning techniques.