AI shows forestry operators best routes in forestFriday 6 Sep 2024
Researchers at the University of Helsinki are developing machine learning methods enabling the harvester to assist the operator in choosing routes that are optimal for both harvesting and nature. The machine could then predict the relevant terrain characteristics even before the actual operation. ’This information will help to optimise the route and assist the operator by telling, for example, where the ground is too soft,’ says Professor Jukka Heikkonen, in charge of the project funded by the Research Council of Finland. ’Harvesting operations must be planned so as not to leave too invasive strip roads. The softer the ground, the more difficult it is for the harvester to travel and the more likely it will cause ground damage,’ Heikkonen notes. Strip roads, or the tracks left by the harvester, are detrimental to forest growth, increase the risk of diseases and are an eyesore. Travelling across soft ground also increases the rolling resistance and therefore, the fuel consumption and cost. The softer the ground, the more probably it will be damaged. The physical quantity used in the study is the rolling resistance factor of the harvester, which describes the ease of travel on a particular stretch of ground,’ Heikkonen says. According to the Finnish Forest Act, only one fifth of the length of a strip road created to reach a harvesting site may be over ten centimetres in depth. For peatlands, the corresponding depth is twenty centimetres. Using the figure for ease of travel and the harvester’s rolling resistance, it is possible to create a map showing the conditions most suitable for the strip roads. The map may be used to position the route of forwarders and to schedule the operation. Measurements of damage caused by strip roads may be combined with open-source forest data and harvester data. This will allow predictions of future damage from the strip roads in a harvesting operation. More >> Source: Foreset.fi Image credit: Finnish Forest Association  | ||
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