Recreational fishing (RF) involves at least 226 million participants worldwide, it generates an annual economic value of 5.9 billion euros in EU and 29 billion euros in the US. Until recent years, studies show that RF has nearly invisibly contributed to approximately 12% of global fishing removal; and it has caused 27% to declining in stock of two endangered species. We have identified 5 main barriers in achieving sustainability in RF: - No or limited real-world georeferenced and time-tagged catch data. - No or limited tools for monitoring and control RF activities. - No or limited communication channels. - Limited knowledge about where to fish. - Post-reactive, not predictive or proactive. The FiskHer App could be an good tool in tackling the 5 barriers towards sustainable RF. We develop a new digital service – FiskHer.ai – leveraging state-of-the-art data science and machine learning technologies for promoting and facilitating of sustainable marine recreational fishing. The idea is to discover scalable machine learned models that accurately predict most probable RF spots with high spatial and temporal resolution in Norway and worldwide. We achieve this via synergized transformation of the domain expertise earned in field owned by FiskHer AS to machine learned models, which are incrementally learned continuously lifelong with user contributed new data. The expected results of the project are a new integrated large dataset with validated labels, new underwater 360 degree video and hyperspectral imaging data for a few fishing spots, software for data integration and automated machine learning, scalable machine learned models for accurate and spatio-temporal resolved predictions. This innovation is most important to us because: 1. it allows us to go from static to dynamic areas. 2. It gives us a wonderful tool that will save us years of manual registration. It will thus be cost-effective and innovative for us, and enable us to deliver many years ahead of schedule.
Project leader: Boyan Yuan
Institution: FISKHER AS