The concept of Precision forestry involves the use of high-resolution data to make precise decisions based on data from digital twins of forests at tree level. The goal of the project is to develop a platform for an end-to-end AI for forest analysis and synthetic digital twins of the forest. Which creates prerequisites for an end-to-end AI for high-resolution analysis of forest data for need-based areas of use.
The problem with precision forestry today is that the ability to collect training data is limited. Another problem is that the lifespan of data in the forest is short as the trees are constantly growing.
The project breaks completely new ground and contributes to previously missing knowledge about how synthetic data generation can be used to build applications in modern forestry. Furthermore, it will take into account the granularity between different tree species and be able to create realistic forests as a result. The value will be that the forest industry can evaluate between optimizing to secure higher economic profitability while at the same time setting aside forest for carbon sequestration, securing carbon sinks in wood for industry, increasing biological diversity, and assisting with nature experiences for society.
The project aims to realize a solution that contributes to the forestry companies' competitiveness through streamlining forest work, and the project's goals contribute to increased competitiveness for Swedish companies in a world market where more people can use the process and the knowledge behind the technology for internationalization where heavy and advanced analyzes are required.