As commercial kelp farms continue to expand in Norway, autonomous methods for monitoring of growth, biomass and biofouling become increasingly important. This project aims to develop optical methods for these purposes.
Cultivated seaweed (including kelp) is considered one of the largest un-exploited sustainable biomass resources for providing food and energy to a global population that is predicted to reach 9 billion people by 2050. Although seaweed cultivation is well established in Asia, European nations are hoping to catch up. Norway, with its long coastline and cold waters, is one of the nations that is leading the way. Key to Norway’s competitiveness will be its ability to implement high tech, autonomous solutions to kelp farming.
Commercial kelp farms need to be monitored regularly. During this process, measurements are taken of kelp biomass, growth and biofouling. These measurements allow predictions to be made about the size and quality of the harvest, and also provide valuable growth data to farmers – which can be used to optimise the biomass of future harvests. Current monitoring methods, however, are labour intensive, destructive (requiring valuable biomass to be removed from the farm) and inefficient. Autonomous solutions are, therefore, required as kelp farms continue to grow in scale.
The main aim of this research project is to take some major steps towards autonomous underwater monitoring of kelp-farms, by using state of the art underwater optical sensors such as underwater hyperspectral imaging (UHI), mounted on a remotely operated vehicle (ROV), to estimate plant size, biomass, health, growth and biofouling. These techniques will be refined at a commercial kelp farm, run by Seaweed Solutions, on the island of Frøya in Trøndelag. It is hope that these techniques will serve as a “proof of concept” where, in the future, stakeholders can use these to maximize harvestable biomass and implement potential biofouling mitigation strategies.
The project aims to apply already established protocols for Underwater Hyperspectral Imager (UHI) and adjust and validate them for the use on two kelp species, in the context of performing autonomous monitoring of biomass, growth and biofouling in a commercial kelp farm in central Norway. These protocols will then be available for stakeholders wishing to implement regular autonomous monitoring of their own farms using UHI. Once these protocols have been refined, they will be used for monitoring macroalgal growth and will address issues that currently limit seaweed biomass production in Norway: seasonal difference in growth conditions and biofouling detection/prediction. These will be addressed by surveying and quantifying key parameters, such as macroalgae health and growth rate and biofouling (detection and estimation of coverage), in addition to environmental conditions that impact macroalgal growth and bryozoan settlement: nutrient concentrations, temperature, salinity and phytoplankton composition and biomass (chlorophyll a). Ultimately, we aim to construct a simple user-friendly method for estimating optimum deployment and harvest times in order to optimize growth and minimize biofouling. This method will serve as a tool for Norwegian seaweed farmers to maximize production, regardless of their geographical location.