Novel Resi-tool data processing to predict timber quality | UniSC | University of the Sunshine Coast, Queensland, Australia

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Novel Resi-tool data processing to predict timber quality

The standard structural grade ranking of Australasian pine is defined by its density and stiffness, measured by the Modulus of Elasticity (MoE). The MoE dictates the mechanical grade or strength of the board.  

The novel Resi-tools project assists forest growers, plantation managers and processors to identify construction grade-quality timber more quickly and easily, and at a much earlier stage, to maximise profitability.

Using the commercially available Resi-tool to collect data, our research has developed and tested algorithms to quickly analyse the Resi-tool data to give commercially useful information on wood quality for the forest industry.

Resi-tool, is a sophisticated power drill instrument which can quickly and non-destructively collect data from plantation grown hard- and softwoods trees.

Wood stumps

This data – when processed by algorithms developed by the project team – allows accurate prediction of whether a section of forest (known as a compartment) will produce high value, high quality construction grade timber when processed.

Similarly, the algorithms can also accurately predict if a compartment will produce low value, low-grade timber when processed.

This allows the growers to determine ahead of time the processing method the logs of compartments will be destined for.

A compartment containing high quality, high value construction grade timber will be assigned to sawmill processing to produce as construction materials.

Compartments identified as low grade can be assigned to an alternate lower cost processing method, for example chipping to produce wood chip.

This foresight allows the producers to avoid high-cost processing of low-quality timber.

Commercial sawing trials run by the project team with the growers and processors have verified the predictions of the algorithms developed by the project team.

Using our teams’ algorithms, a one percent improvement in plantation wood quality and quantity is envisaged for growers which could easily translate to a five percent improvement in recovery of high-quality timber for processors.

Both business groups are multi-billion-dollar enterprises so our research should deliver multi-million-dollar benefits for the industry.

This study is a first of its kind in the world and our findings of ‘a commercially useful correlation between the non-destructive drilling resistance of trees in the field and wood quality perceived by the processors’ have been adopted by 60 percent of the 1M ha Australian softwood industry.

This technology potentially allows the growers to increase the number of rotations in a century (currently this is two to three per century) but use of the Resi technology platform could increase this to four rotations per century as the forest compartments could be harvested when they are producing good quality (high MoE) wood rather than when they reach a specific age or size class.

Growers are now actively screening their plantations with this goal. This has significant positive wood flow and quality implications.

Our process utilising Resi-tool data to predict the construction grade quality more quickly and accurately, has enabled foresters to make improvements in the planning and management of plantations to meet market needs, improved decision making around thinning (particularly in identifying which compartment to harvest early as they will not yield a sufficiently high-quality timber), and improved efficiencies and recovery in the sawmills (i.e. more construction grade timber is produced from fewer logs).

This has, and will continue to improve profits for forest growers, plantation managers and wood processors and has flow on impacts for the building industry as there is more demand that the growers can supply with Australia currently running a $2B deficit in timber and timber products.

Chief investigators: Dr David Lee and Dr Vilius Genvilas.

This research is out of UniSC's Forest Industries Research Centre


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