Tavoite

AGRARSENSE will focus on developing European SoA technologies for future agricultural needs, encompassing various agricultural and forestry related challenges around them, as well as the enabling technologies of ICT infrastructure and electronics hardware that will be critical for future business and economic needs. This project drives the agenda for rapidly emerging agricultural growth in electronics, AI and automation and analytical tasks.

The goal of the AGRARSENSE project is to develop sensor and decision-support technologies and enablers for smart farming with a holistic approach that is concretely demonstrated in seven use cases. These use cases in AGRARSENSE are focused on developing solutions for the following innovation problems with significant potential business impacts:

– How greenhouse operations could be improved? (UC1)

– How vertical farms could improve resource exchange? (UC2)

– How to improve the monitoring capabilities in vineyards? (UC3)

– How to increase the level of automation of agricultural robots? (UC4)

– How to improve the operations and safety in forestry? (UC5) – How to reduce the impact of (or damage) organic soils? (UC6)

– How to optimize the use of fertilizers? (UC6)

– How to monitor and control water pollution? (UC7)

Tulokset

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 ammattikorkeakoulu

2025 AGRARSENSE UC5 (Forestry) video: AGRARSENSE use case 5 Forestry 2025 demonstrator video D8.12

2025 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 Operations

Pohjoisen 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 digitalisaatiota

2025. Making realistic 3D trees. Santeri Valkama, asiantuntija.

Making realistic 3D trees