Stora Enso’s AI model identifies damage caused by spruce bark beetles with an accuracy level that can pinpoint the condition of individual trees.
Stora Enso’s AI Precision Forestry project combines available forest-related digital material from various sources, such as laser scanning, satellite imagery, aerial photographs and harvester data.

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By combining different data sources, accurate and up-to-date information on forests is produced for different uses in both Sweden and Finland. In Lappeenranta, an artificial intelligence model detects spruce bark bark beetle damage accurately.
Stora Enso started a project to combine digital forest data in 2023, and the results are promising. In Lappeenranta, data combined with an artificial intelligence model has been tested to identify spruce bark beetle damage. The spruce bark beetle is an insect pest that attacks weakened spruces, windfalls or stored timber. The use of the AI model will next be expanded to the entire region of Southern Finland, where the risk of insect damage is the greatest.
“As it reproduces, the spruce bark beetle is also capable of destroying vigorous spruces. Spruce trees may die in a large area under favourable conditions even during one summer if the spruce bark beetle population is large. Our AI model enables the identification of dead trees with the accuracy of up to one tree,” says Juha Kaipainen, Purchasing Manager for the Lappeenranta region, Stora Enso’s Wood Supply Finland.

Stora Enso also utilises AI models in other ways, such as updating forest resource information or mapping forest biodiversity sites. In Sweden, Stora Enso uses an artificial intelligence model to produce accurate forest plan information for private forest owners, because the same kind of public forest resource information is not available there as in Finland. Stora Enso owns 1.4 million hectares of forest in Sweden, and the collected data is also used in its own forests to update the forest boundaries.
“We have already come a long way in our work. In the future, the technology can be used extensively, for example, to assess the need for seedling stand management, advance planning of forest machine routes and more accurate monitoring of the state of forests,” says Antti Kaartinen, Chief Specialist of Stora Enso’s Precision Forestry project.
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