According to researchers, smart harvesters will make traditional forest inventory nearly unnecessary, as topographic data will be automatically collected in connection with harvesting and forest management up to the accuracy of individual trees. Automated systems will reduce operators’ workload. The accurate forest data can be used to plan logging, prepare forest inventories and monitor the biodiversity of forests.
In the IlmoStar project, researchers from the Finnish Geospatial Research Institute (FGI) of the National Land Survey of Finland, the University of Eastern Finland and Natural Resources Institute Finland developed and tested the positioning and computer vision of a forest machine in collaboration with Ponsse Plc. The forest machine was fitted with sensors that allowed it to collect data in connection with harvesting for the measuring and monitoring of indicators such as forest biodiversity, carbon sequestration potential and thinning intensity.
– We have achieved good results through collaboration between researchers and companies. The research and development of a smart forest machine is a good example of how companies can utilise the latest research data when developing new technologies, says Research Professor Harri Kaartinen from FGI.
– Exploring the possibilities of future harvesting technology is important. The development of forest machine technology requires perseverance, and the project has provided us with valuable ideas and guidelines for this development work. We have already taken advantage of the project’s research results in the improvement of the positioning of forest machines, says Chief Design Engineer Joni Backas from Ponsse.
– Harvester operators felt that system-based support for the monitoring and reporting of thinning intensity was necessary. It can also be utilised for operator guidance. Accurate tree-level data combined with the trunk data produced by the harvester form the basis for the thinning intensity monitoring concept developed by the project, says Senior Scientist Kari Väätäinen from Natural Resources Institute Finland.
Five examples of future technological upheavals in forestry – possible by 2035
– As part of the IlmoStar project, researchers assessed what benefits forest machines could offer by 2035 if sufficient RDI investments in technologies are made and close cooperation takes place in the industry, says Professor Kalle Kärhä from the University of Eastern Finland.
1. Topographic data is automatically collected and produced when working with a machine
In 2035, there will be transparent reporting on the quality of harvesting operations to forest owners and the authorities, who will no longer carry out any national forest inventories themselves. Instead, the data will be automatically produced in connection with harvesting and forest management operations up to the accuracy of individual trees.
2. Smart harvesters do the work, making the operator’s job easier
Guiding and automated forest machine systems will make the operator’s work more efficient, improve the quality of harvesting and improve the operator’s ability to cope with the work. For example, the planning of the driving path network and related guidance, real-time monitoring of thinning intensity, the identification of trees of poor quality and sick trees, the preservation of trees that are valuable in terms of forest biodiversity and the creation of forwarder loads will be almost completely automated, no longer requiring any special attention from the machine operator.
3. Harvesting optimised through artificial intelligence and automation
By 2035, advances in AI will have added more automated functions to harvesting planning systems, which will help the optimisation of the selection and timing of harvesting sites.
4. Forest resource data up to the accuracy of individual trees
The accuracy of forest resource data will significantly increase to cover the quantity and quality of trees, changes, harvestability and the soil. Data on individual trees collected via airborne laser scanning, drones and other aircraft will enable the planning and realisation of cost-effective, low-carbon harvesting.
5. Forest machines collect data about their surroundings and refine existing forest resource data
According to the vision, precision positioning will be in use in all harvesters by 2035. Forest machine sensors based on laser and computer vision technologies, which are able to enrich the advance data sent to the machines, have started to become more common but are not yet in use in all forest machines.
Photo: Valtteri Kinnunen University of Eastern Finland
Additional information
Research Professor Harri Kaartinen, Finnish Geospatial Research Institute of the National Land Survey of Finland, firstname.lastname@maanmittauslaitos.fi, +358 29 531 4756
Professor Kalle Kärhä, University of Eastern Finland, firstname.lastname@uef.fi, +358 50 475 4772
Senior Scientist Kari Väätäinen, Natural Resources Institute Finland, firstname.lastname@luke.fi, +358 50 3913259