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Automating wood quality assessment

Automating wood quality assessment

Modulai has in collaboration with Tracy of Sweden AB developed a system to automatically determine wood quality and count age rings in tree logs. By optimizing the sorting process based on wood quality, sawmills can efficiently allocate high-quality wood for premium products and lower-quality wood for other applications.

Background

Tracy of Sweden, a company focused on extracting the greatest planetary and economic value from forests, sought to develop a toolbox of algorithms for automatic log quality classification. Their goal was to leverage inexpensive camera technology and AI advancements to assess key quality aspects such as annual ring density, rot, split logs, blue stain, insect damage, root flares, and cracks. These quality issues can significantly impact the value and usability of the harvested wood, leading to inefficiencies and waste in the forestry industry.

 

 

Solution

We developed a multi-stage system to tackle the highest value problem of counting the age rings that largely determines the wood quality.

The process involves:

  1. Log surface segmentation: A UNET segmentation model isolates the cutting surface by removing the background (in place at project start).
  2. Pith detection: An EfficientNet model locates the log’s center.
  3. Ring counting: Two methods were employed:
    1. Measuring light intensity radially from the pith to count the number of dark segments i.e. rings.
    2. Training a ring-segmenter UNET model on synthetically generated data using GAN models.

Result

Our system achieved an accuracy of 80% in classifying log quality, matching the performance of human operators. While achieving 100% accuracy is challenging due to factors like damaged wood fibers or surface obstructions like snow or dirt, the system can work in tandem with operators to alert them of significant discrepancies. Tracy is seeking partners interested in utilizing this system in their operations.

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