AGRARSENSE – Smart, digitalized components and system for data-based Agriculture and Forestry Links related to the content Move to project page More info Anne Saloniemi Schedule 1.1.2023 -28.02.2026 UN sustainable development goals Studies Natural Resources, Forestry Expertise group Future Bioeconomy Funding sources Business Finland Project status Finished Competence lead Responsible forestry and logging Results The AGRARSENSE project was an EU-funded consortium under the Chips Joint Undertaking that developed advanced microelectronics, photonics, sensor technologies, and data-management solutions for agriculture and forestry. The project was implemented in cooperation between industry partners and research institutions through international and national networks, according to needs identified by industry.AIFor was a Business Finland–funded R&D project implemented by Lapland University of Applied Sciences (Lapland UAS). The main goal of the project was to develop new methods and a high-fidelity virtual forest environment (a digital twin) that can be used to train AI for autonomous forest machines — especially to enable reliable autonomous navigation and operation in complex, unpredictable forest conditions. The work combined detailed 3D modelling (including trees, ground vegetation, fauna and obstacles), development of a simulator platform and data pipelines, and modelling of forest machine interaction and relevant sensors (e.g., RGB cameras, depth/segmentation, LiDAR/radar) with data driven noise models to capture effects of harsh conditions such as rain and snowfall. A central element was a real site demonstration in which the digital twin and simulated outputs were compared against a real logging site to evaluate authenticity and applicability. 2026 AIFor eBook: Digital Solutions for Forestry – Lapin ammattikorkeakoulu2025 AGRARSENSE UC5 (Forestry) video: AGRARSENSE use case 5 Forestry 2025 demonstrator video D8.122025 Forestry simulator for AGRARSENSE project: New video: Forestry simulator for AGRARSENSE project! — FrostBit 2024 Lapland UAS Private 5G: Enabling Safe and Reliable Next-Gen Robot Operations: Lapland UAS Private 5G: Enabling Safe and Reliable Next-Gen Robot OperationsPohjoisen tekijät – Lapin AMKin asiantuntijablogi:2024. Developin a simulator platform to advance autonomous system in forestry usage. Aleksi Narkilahti, asiantuntija.Developing a simulator platform to advance autonomous systems in forestry usage 2024. 5G menee metsään. Tommi Uusitalo, asiantuntija.5G menee metsään 2025. Perinteiset metsänmittausmenetelmät tukevat metsätalouden digitalisaatiota. Veli-Pekka Karjalainen, opettaja.Perinteiset metsänmittausmenetelmät tukevat metsätalouden digitalisaatiota2025. Making realistic 3D trees. Santeri Valkama, asiantuntija.Making realistic 3D trees