Low-grade wood is cheap — but variability, defects, and changing appearance make manual selection inconsistent. This case shows how to extract usable regions reliably at production speed.
Business background
- Wooden pallets do not require premium lumber, so it’s economically smart to use lower-quality raw material.
- Low-grade wood contains defects and variability that can reduce yield and disrupt production.
- The goal is to extract the best usable parts from very low-quality wood while keeping throughput high.
- Manual selection is inconsistent, and fixed-threshold rules fail when appearance changes.
What this demo demonstratesScreenshot from 2026-02-16 13-17-10
- Automated selection of usable regions from low-quality wood based on structural properties.
- A decision pipeline designed around domain knowledge of wood structure (grain, texture, typical defect patterns).
- Mathematical / fuzzy-logic algorithms instead of AI networks — chosen for controllability and explainability.
- Integration into production lines via standard industrial protocols (Modbus, CAN bus, OPC-UA, Ethernet/IP).
Benefits for end users
- Higher yield from low-grade input by consistently extracting the best parts.
- Lower material cost without sacrificing pallet reliability targets.
- More predictable output quality and fewer rejects reaching later stages.
- Transparent decision rules that engineers can tune as raw material changes.
What AgirVision can deliver
- Process analysis to define what “usable” means for your product and line constraints.
- Algorithm design using classic vision + fuzzy logic where it makes sense (or AI where it adds real value).
- Industrial integration using standard protocols such as Modbus, CAN bus, OPC-UA, or Ethernet/IP.
- Deployment support to connect perception outputs to sorting, cutting, routing, or operator guidance.
Want something similar? Send your constraints (line speed, camera setup, defect types) and we’ll outline the shortest path to a deployable solution.